Minfy declared a Growth Champion 2023 by Economic Times and Statista!

Minfy is amongst India's Fastest Growing Companies (32nd) as per a study run by The Economic Times and Statista and is listed amongst India's Growth Champions 2023.

Economic Times and Statista pick and rank a select group of businesses that have perfected the art of gaining ground year after year. Aside from growth, key considerations are that businesses have real economic substance, sustainability, and legitimacy.

Thank you The Economic Times and Statista.

#Indiagrowthchampion2023 #betterfocusbettergrowth #bionicforabetterworld #economictimes

Click here to see the detailed rankings.
https://lnkd.in/gvGq9_5q

Demand forecasting is a common use case of AI-ML. It can be used to identify areas of improvement and best practices that can help businesses improve its forecasting accuracy. It helps in production planning and determine incremental impacts of new initiatives, plan resources in response to expected demand, and project future budgets.

The following image depicts a simple forecasting cycle. It is all about using historical data, train forecasting model on it, generate forecast to make informed decisions. It’s a cyclic process — when new data arrives, we feed it back into the forecasting model and retrain it so that the model remains up to date.

Demand forecasting cycle

A frequent question we get from customers, consultants and other business experts is “What would you consider a good level of forecast accuracy?”

However, it may not be the most relevant question to raise, though. First of all, forecasting is seldom the objective in any retail or supply chain planning scenario. It is always a means to an end. A forecast is only useful if it helps us accomplish other objectives, like increased shelf availability, accurate production planning, less wastage.

While forecasting is a crucial component of all planning activities, it is only one cog in the machine, which means that other factors like changes in consumer behaviour, economic turndowns, historical data quality can have a significant impact on the forecasting accuracy.

What degree of forecast accuracy can actually be obtained depends on a number of factors. This is one of the reasons why comparing forecast accuracy between businesses, or even between products within the same business, is so challenging.

When integrating demand forecasting systems, it’s essential to understand that they are vulnerable to unpredictable situations. So, the demand forecasting machine learning models should be re-trained according to current reality.

With large forecast horizon, the probability that future demand will be impacted by developments that we are not currently aware of grows noticeably. Weather-dependent demand is a straightforward illustration. If we need to make decisions on what quantities of summer clothes to buy or produce half a year even longer in advance, there is currently no way of knowing what the weather in the summer is going to be.

On the other hand, if we were in charge of overseeing the replenishment of ice cream in grocery shops, we could utilize short-term weather forecast to estimate how much ice cream to transport to each location.

Important features to consider while training ML model for forecasting:

Seasonality

Seasonality refers to the regular and predictable pattern of demand for goods or services that occurs at certain times of the year, such as holidays, weather changes, or cultural events. What’s more, understanding of seasonality for each of your products can give you a head start in demand planning. With a good grasp of how much consumer demand varies, you can select the right demand forecasting models, focus on making plan adjustments and spot true outliers that require further attention.

For example, a business that sells winter clothing may experience a significant increase in demand during the winter months and a decrease in demand during the summer months. Similarly, a business that sells ice cream may experience higher demand during the summer months and lower demand during the winter months.

Weather data feature effect

Demand will typically vary from region to region, depending on local calendars and weather. You may need to segment your data geographically to identify seasonality patterns. At the same time, aggregating forecasts to a less granular level — product category instead of product, for example — may make it easier to distinguish seasonal patterns from random noise.

Rather than including only historical time series and demand, incorporate weather data into a machine learning model for demand forecasting as the model can account for the impact of weather on consumer behaviour and give better predictions accordingly. This can lead to help businesses make better decisions about inventory management, marketing strategies, and other aspects of their operations.

Furthermore, weather data can be used to create more granular and accurate models by breaking down the weather into different variables such as temperature, precipitation, humidity, etc. This allows for a more nuanced analysis of how weather affects demand for different products or services.

The following code snippet reads weather data from a csv file and pre-processes it by resampling to daily frequency and imputes the missing values by linear interpolation imputation technique, so that it can used with the historical time-series data to train a forecasting ML model.

Code snippet to pre-process weather feature

Location

For certain use cases, location is an important feature when training a demand forecasting ML model.

Competition & Vendors: The location of a business and its associated vendors location where a particular product is being sold can play a vital role. Also, if there are many similar businesses in the area, the demand for a particular product or service may be lower than in an area with few or no competitors.

Demographics: The demographics of an area may play a crucial role in determining the demand for a product or service. Different age groups, income levels, and cultural backgrounds have different preferences, and this can be reflected in demand patterns.

Infrastructure: The availability and quality of infrastructure such as roads, transportation, and logistics can also affect demand. Areas with poor infrastructure may have lower demand due to difficulties in accessing or transporting products.

Holidays

Sales patterns can vary significantly during holiday seasons due to changes in consumer behaviour, such as increased shopping activities, changes in preferences, and higher demands for specific products or services.

Including holiday data into the machine learning model helps to improve the accuracy of sales forecasts during these periods, which is essential for businesses to plan their operations and inventory management. Additionally, taking holidays into account in the model can also reveal important insights into customer behaviour and preferences, which can help businesses tailor their marketing strategies to better engage with their target audience.

Trends

One of the benefits of including trends in demand forecasting algorithms is the ability to anticipate market changes and respond accordingly. For example, if a trend analysis indicates a shift in consumer preference for a particular product, the company can adjust their production and marketing strategies to meet the new demand. Additionally, trend features can help identify whether changes in demand are due to short-term fluctuations or long-term changes in the market.

Moreover, trends can also help identify potential bottlenecks in the supply chain, which can help companies optimize their inventory levels and reduce waste. By accurately forecasting future demand, companies can avoid stock outs or overstocking, which can lead to significant losses.

Let’s say you have a dataset that contains daily temperature readings for a specific location over a period of four years. You want to include a feature that represents the trend in temperature over time. One way to do this is to calculate a moving average of the temperature readings for each day, using a window size of, say, 30 days. This will give you a new feature that represents the average temperature over the past 30 days, which can be a good indicator of whether the temperature is trending up or down.

Code snippet to pre-process Temperature trend feature

Above code snippet, reads the ‘temperature_data.csv’ file which contains a column called ‘temperature’ that has the daily temperature readings. The rolling () function is used to calculate a rolling average of the temperature column with a window size of 30. The resulting rolling average is then added as a new column to the data frame, with the name ‘temp_trend’.

Machine learning models/tools that can be used for demand forecasting

• Amazon Forecast: Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis. Amazon Forecast uses machine learning (ML) to generate forecasts with just a few clicks, without requiring any prior ML experience. If you have a time-series forecasting use case and you don’t have much experience in machine learning, Amazon Forecast is likely a good choice.

• Amazon SageMaker:It is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily train and test machine learning models, and then directly deploy them into a production-ready hosted environment. If you require more flexibility and control over your machine learning models, add and create features, built relationship between the features then Amazon SageMaker can be a better fit.

• Amazon Deep AR: The Amazon SageMaker Deep AR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN).

• ARIMA: An autoregressive integrated moving average model is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables. The model’s goal is to predict future data points by examining the differences between values in the series instead of through actual values.

• SARIMA: Seasonal Autoregressive Integrated Moving Average, SARIMA or Seasonal ARIMA, is an extension of ARIMA that explicitly supports univariate time series data with a seasonal component. It adds three new hyperparameters to specify the autoregression (AR), differencing (I) and moving average (MA) for the seasonal component of the series.

• LSTM’s: Long Short — Term Memory is a type of neural network architecture that is commonly used for time series forecasting. LSTM networks are particularly useful for dealing with time series data that have long-term dependencies or complex patterns. The basic idea behind LSTM is to use a series of memory cells to remember and store important information about the input sequence, and then use this information to make predictions about future values in the sequence.

Conclusion

Demand forecasting is a crucial aspect for any business operation, and the use of machine learning algorithms can significantly improve the accuracy and efficiency of the forecasting process. By considering historical data, current trends, and other relevant factors, businesses can better understand their customers’ needs and optimize their production and inventory levels to meet demand while minimizing the loss.

You should ensure that the process of re-training your ML model with new data happens at continuous intervals. The goal of model retraining is to adapt the model to changing patterns in the data, or to improve its accuracy as new data becomes available.

Retraining a model is important because machine learning models are only as good as the data they are trained on. It can also help to address issues such as model drift, where the performance of a model deteriorates over time due to changes in the data distribution.

In conclusion, demand forecasting using machine learning is not a one-time process, but a continuous effort that requires ongoing monitoring and retraining of models on latest available data. By leveraging the latest technologies and insights, companies can stay ahead of the curve and thrive in today’s dynamic and rapidly evolving marketplace.

- Author Yasir Ul Hadi

References:

Revolutionizing the Energy and Resources Industry: How AI is Reshaping the Future of Sustainability.

The energy sector has never been sceptical in adopting new technologies in the past. Be it digitization of the power generating infrastructure, exploring renewable sources of energy or innovating the aged infrastructure of transmission and distribution, it has always embraced these challenges and adopted well. However, challenges such as high carbon emissions, inefficient energy management, aging infrastructure, etc still needs to be addressed to build a more sustainable future. These challenges can be dealt with innovation and collaboration amongst stakeholders from different domains.

Artificial intelligence (AI) has the potential to transform the energy sector as it can address a wide range of problems that the sector is currently facing. The industry has already started to embrace the power of Artificial Intelligence (AI) to drive efficiency, reduce costs, and minimize carbon emissions.

According to Precedence Research, the global artificial intelligence (AI) in renewable energy market size is projected to surpass US$ 75.82 billion by 2030 and is expanding at a CAGR of 27.9% from 2021 to 2030.

In this article, we will explore some of the ways in which AI can help address the challenges that the energy sector is facing. Overcoming these challenges can create a more sustainable future for all. But before that, let's have a quick look at some of the key challenges in the energy sector.

1. Centralized power generation – majority of the global grid network relies on a few power generators making the entire infrastructure highly centralized. Though we have done a great job building such a great infrastructure, large, centralized networks have their own challenges. Some of these are high loss of energy from generation to distribution, high carbon emissions as most of these power plants are based out of non-renewable sources of energy, low power conversion rate, etc.

2. Challenges while transitioning to renewable sources – as the share of renewable sources of energy increases at a fast pace, integrating it with the existing grid system without destabilizing the grid, is quite challenging. Not only that, maintaining balance of demand and supply gets even more challenging as outputs of renewable sources of energy depends on external factors such as weather.

3. Energy Loss and theft: Improving energy efficiency is a key challenge for the energy sector. There is a need to reduce energy waste and increase the use of renewable energy sources to meet the growing demand. Theft and unethical usage of power is again a key concern causing losses to the power suppliers.

4. Ageing infrastructure: Much of the energy sector's infrastructure, such as power grids, equipment and pipelines, is ageing and in need of upgrades. This can be costly and can also create reliability issues.

Now, let’s see how AI can be leveraged to address these challenges in the energy sector.

• Renewable energy forecasting: As the share of renewable energy is increasing at a fast pace, integrating it with the existing network is challenging. One of the key reasons is the fact that the production of energy from these sources is not consistent (solar and wind power plants, their power output depends upon external weather factors). Because of this, often the forecasts are inaccurate leading to either blackouts or wastage of surplus energy. Hence, accurately forecasting the output of wind, solar, and other renewable energy sources is essential for managing energy supply and demand. AI algorithms can be used to analyse historical data and real-time weather patterns to predict renewable energy output with greater accuracy, allowing grid operators to balance supply and demand more effectively.

• Demand forecasting: AI algorithms can be used to forecast energy demand in real-time, helping grid operators to manage supply more efficiently and prevent blackouts. This can help balance supply and demand, reduce the need for backup power, and improve the overall reliability of the energy grid. This also helps in accurately planning purchase of power for different durations like intraday, short term, and long term. Other than demand forecasting, price forecasting is another important use case that helps in creating more informed power purchase agreements.

Let's have a look at the high-level architecture for demand forecasting using AWS services. With the help of AWS services like Forecast, Quicksight, Sagemaker, etc we can quickly build and deploy AI solutions at scale. Also, to make things easy, let’s assume that we are trying to forecast power demand for a utility.

The architecture consists of four broad functional components —

• Data Preparation – First step is to prepare the demand data or the target time series, along with this we may add related time series data such as weather data, income data, and other metadata. Once this data is prepared for training, the data is uploaded to a S3 bucket from where it is used for machine learning and data visualisation tasks.

• Model Training – Once the data pre-processing is done, use AWS Forecast to create a predictor or train a custom ML model using a Jupyter notebook with SageMaker. Once the model is trained, endpoint configuration is set and the endpoint is deployed to generate forecast. Amazon CloudWatch is used to monitor the model training and its metrics and Amazon SNS (Simple Notification Service) is used to send or receive notifications for training related events.

• Forecasting/Inferencing – we can write a simple Lambda function that is triggered by S3 event notification. Whenever new data is uploaded to that S3 bucket, the Lambda function will be triggered to generate a forecast through AWS Step Function workflow which is a service to orchestrate tasks. All we need to do is upload data to the S3 bucket.

• Data Visualization – Once a forecast is generated, the results and related metrics are then saved to the s3 bucket and Amazon Athena is used to query this data. After querying the results, Amazon Quicksight is used to visualize forecast results.

• Predictive maintenance: Failure of electrical equipment leads to all sorts of undesirable conditions. These failures can lead to electrical safety hazards, unplanned downtime, increased maintenance costs, decreased equipment reliability and life span. Old predictive maintenance techniques were reliable in past but are now outdated. With reduced costs of sensors in the industry, AI solutions such as predictive maintenance can replace these old solutions and can transform entire system’s efficiency.

• Predictive maintenance uses machine learning algorithms to predict when equipment is likely to fail, based on historical data and real-time sensor readings. By identifying potential issues before they occur, maintenance teams can schedule repairs during periods of low demand, reducing downtime and costs. Also, equipment running under healthy and efficient conditions will have longer lifespan, and reduced running costs.

• Energy optimization: AWS’ collaboration with Carbon Lighthouse to reduce carbon emissions by optimizing energy usage with the help of Artificial Intelligence is a good example of energy optimization. AI solutions can be deployed to identify unoptimized scenarios and make them more efficient. Energy optimization involves using AI algorithms to optimize energy consumption across various processes, such as heating, cooling, and lighting. These algorithms learn the underlaying pattern and works to reduce power consumption by the overall process. This leads to reduced energy waste, improved efficiency, and lower costs.

• Fraud Detection: Electricity theft and fraud is not a new challenge, it has been there for a long time and fraudsters keep innovating newer methods of unaccounted power consumption. However, with the help of new technology such as AI, more robust anti-theft systems can be built to help minimize such events. AI can be used to detect fraudulent activity in the energy sector, such as meter tampering or theft of service. By analysing usage patterns and other data, AI can identify suspicious behaviour and alert utilities to potential problems, helping to reduce losses and improve overall revenue.

• Power Management – Wastage of power due to unoptimized solutions or human ignorance is a key concern when it comes to power management. When AI is integrated with sensors (IoT), we get a complete power management toolkit that can be installed anywhere from residential houses to commercial buildings. These AI powered smart solutions can help companies to cut down their utility bills by a significant amount. Till we have “arc-reactors” powering our buildings like Tony Stark’s NYC lab, we need an AI to at least manage the power of our buildings.

The energy sector is facing significant challenges in the modern world, including high centralization, transitioning to renewables, energy losses and thefts, and ageing infrastructure. To overcome these challenges, the industry must embrace innovation and collaborate with stakeholders to create sustainable solutions. AI is one such innovation that can help reduce carbon emissions, optimize energy production and distribution, manage renewable energy sources, and improve energy efficiency. By leveraging the power of AI, the energy sector can create a more sustainable future for all, while also ensuring reliable and secure energy supplies. The road ahead may be challenging, but with innovative solutions and collaborative efforts, we can build a brighter and cleaner future for generations to come.

— Author Rishi Khandelwal

How AI is Changing the Game in Chronic Disease Care

As the world continues to grapple with the ever-increasing burden of chronic diseases such as diabetes, heart disease, and cancer, it becomes increasingly apparent that traditional methods of disease management and prevention are no longer sufficient.

According to the World Health Organization (WHO), chronic diseases account for almost 40 million deaths globally and are projected to increase to 57 million by 2020[1]. Furthermore, 80% of all deaths from chronic diseases occur in low- and middle-income countries[2].

The need for innovative solutions that address both the medical and lifestyle factors contributing to chronic disease development is paramount. The complexity of this problem requires a multi-disciplinary approach that takes into account the social, economic, and environmental determinants of health, making it a challenging issue to tackle.

The Challenge

Managing chronic diseases such as diabetes, heart disease, and cancer poses a considerable challenge due to the requirement of ongoing medical treatment and management. This ongoing care can be a significant burden for patients and their families, as it often entails frequent visits to healthcare providers, regular medication consumption, and the adoption of lifestyle changes to manage the disease. Furthermore, lifestyle factors, such as tobacco use, unhealthy diet, and physical inactivity, not only contribute to the development of chronic diseases but also make them more difficult to manage. These changes can be challenging and may require support from healthcare providers, family, and community resources. Addressing the challenges associated with chronic disease management necessitates a holistic approach that addresses both medical treatment and lifestyle factors.

In addition to the burden on patients and families, the rising cost of chronic disease management in India is a major concern for healthcare systems and policymakers. The economic cost of chronic diseases in India, which was around $100 billion just five years ago, is expected to continue to rise in the coming years, further exacerbating these challenges.

The Solution

Preventing the onset of chronic diseases such as diabetes, heart disease, and cancer is a crucial approach to addressing the challenges associated with their treatment and management. However, the overburdened and understaffed nature of healthcare systems makes it difficult to provide the necessary care and support to prevent these diseases from developing in the first place. To more effectively address these challenges, advanced tools and technologies are required to aid and better equip healthcare providers. Artificial Intelligence (AI) is one such tool that has the potential to revolutionize the management of chronic diseases.

Artificial Intelligence technology-based solutions can be leveraged to improve the prediction, diagnosis, and treatment of chronic diseases by providing healthcare professionals with more efficient and accurate tools. Utilizing AI in chronic disease management can also help to reduce costs and optimize resource utilization. It can assist healthcare professionals in making more informed decisions, identifying at-risk patients, and providing more personalized care.

AI-driven solutions have the potential to bridge the gap between the rising costs and the increasing burden of chronic diseases, making them more accessible and affordable for the population. By incorporating AI-driven solutions, healthcare systems can improve patient outcomes and create a more sustainable healthcare system.

There are several Artificial Intelligence technology-based solutions that are currently being used or have the potential to be used in chronic disease management and prevention:

1. Predictive modeling

Predictive modeling in chronic disease care can be used to identify individuals at high risk of developing a chronic disease and predict outcomes through analyzing patient data such as electronic health records. It can be helpful in chronic disease care by identifying individuals at high risk of developing a chronic disease, predicting outcomes, and guiding the treatment and preventive care decisions.

• Identifying high-risk patients: Machine learning algorithms can be trained on large amounts of data, such as patient EHRs, to identify patterns and predict outcomes.
• Predicting progression: Predictive modeling can be used to predict the progression of chronic diseases such as diabetes by analyzing data such as blood glucose levels and medication use.

2. Early detection

Early detection in chronic disease care refers to identifying the presence of a disease or the risk of developing an infection at an early stage, before the onset of symptoms, or before the disease has progressed significantly. Early detection can be beneficial in several ways:

• Improving outcomes: Early detection can lead to more effective treatment and management of chronic diseases, which can improve outcomes for patients. For example, if a patient with diabetes is diagnosed early on, they can take steps to control their blood sugar levels and prevent the development of complications.
• Reducing costs: Early detection can reduce the costs associated with chronic diseases, as treatments and management strategies are more effective when they are initiated early on.
• Increasing access to care: Early detection can increase access to care for patients, as they are more likely to be diagnosed and treated before the disease progresses and becomes more difficult to manage.
• Reducing the burden on healthcare systems: Early detection can also help to reduce the burden on healthcare systems, as patients with chronic diseases that are diagnosed early on are less likely to require hospitalization or other intensive care.
• Improving the quality of life: Early detection can improve the quality of life for patients, as they can take steps to manage their disease and prevent complications before they occur.

3. Personalized treatment

Personalized treatment or I would prefer to call it precision medicine, is an approach to healthcare that takes into account individual differences in genes, environment, and lifestyle. In the context of chronic disease care, personalized treatment can be helpful in several ways:

• Tailoring treatment to individual needs: Personalized treatment can improve treatment outcomes by taking into account individual differences in genes, environment, and lifestyle.
• Reducing side effects: Personalized treatment can reduce side effects by avoiding treatments that are unlikely to be effective or that may be harmful to the patient.
• Optimizing patient outcomes and reducing costs: Personalized treatment can improve patient outcomes by providing the right treatment for the right patient at the right time, thus reducing the risk of disease progression or complications, and reducing healthcare costs by avoiding unnecessary treatments.

4 Remote monitoring

Patient monitoring is the ongoing measurement and tracking of a patient’s health status in order to detect changes and respond accordingly. Supplementing patient monitoring with Artificial Intelligence can enhance the benefits of patient monitoring in chronic disease care by providing real-time data analysis, personalized treatment plans, and early warning signs.

• Identifying changes in health status: AI algorithms can analyze patient data in real-time and generate alerts for abnormal readings or trends, which can help identify changes in a patient’s health status and allow for early interventions.
• Monitoring treatment effectiveness: AI can process large amounts of data and identify patterns that would be difficult for a human to discern, which can be used to adjust treatment plans as needed. This can help to determine the effectiveness of a treatment plan by tracking changes in a patient’s health status over time.
• Improving communication between healthcare providers, patient outcomes, and engagement: AI can provide healthcare providers with real-time data analysis and personalized treatment plans, which can improve communication between healthcare providers and make treatment decisions more informed. Additionally, AI can provide early warning signs and personalized feedback and advice to the patients to better understand their health status and take action to improve it.

The Conclusion

AI is rapidly changing the paradigm of chronic disease care by leveraging its ability to process large amounts of data, identify patterns, and make predictions. Its potential to reduce mortality rates, alleviate the economic burden on healthcare systems, and improve the overall quality of life for individuals affected by chronic diseases, has been widely acknowledged by healthcare providers, policymakers, and community organizations.

From predictive modeling and patient monitoring to clinical decision support, AI is providing new and innovative ways to manage and treat chronic diseases. By utilizing sophisticated algorithms such as machine learning, deep learning, and natural language processing, AI is able to extract valuable insights from complex data sets. This can aid in the development of actionable strategies for disease management and prevention by healthcare providers, policymakers, and community organizations.

As AI technology continues to evolve and become more sophisticated, it has the potential to revolutionize chronic disease care, leading to better outcomes and a higher quality of life for patients. With its ability to provide real-time data analysis, personalized treatment plans, and early warning signs, AI is poised to change the game in chronic disease care, making it more efficient, effective, and personalized.

— Author: Gaurav Lohkna

P.S.: For a more detailed and technical analysis of AI-based solutions in Chronic Kidney Disease management (one of 13 major chronic diseases), kindly await the publication of my upcoming study/article. It provides an in-depth examination of cutting-edge technologies, algorithms, and methodologies in chronic care.

References
[1] World Health Organization (WHO). Noncommunicable diseases: Mortality https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/ncd-mortality
[2] World Health Organization (WHO). Global health observatory data. https://www.who.int/gho/en/

Over the past few years, businesses are digitizing their operations on vast scale, and pharmaceutical sector is no exception to this. Digitization comes with its own set of challenges such as gathering massive data, analyzing it and using the analyzed data to decipher complicated data challenges. Artificial intelligence (AI) has the potential to overcome these challenges. Organizations are using AI technology to handle immense volumes of data with better automation, generate data driven insights, mimic human intelligence and much more.

The wide range of benefits offered by AL is increasing its adoption in almost every business. Today, many leading pharma companies are leveraging AI technology to enhance their processes of manufacturing, drug research and development. A news portal predicts that nearly 50% of global healthcare companies will be adopting AI technologies by 2025.

But why are pharma companies increasingly adopting AI? What is the role of AI in drug discovery?

Let’s get to know through this article. Read on to understand the role of AI in drug discovery, how AI is helping modernize the traditional drug research method and how the overall drug discovery process has become faster, better and cheaper with AI technology.

Applications of AI in Drug Discovery

Why are pharma companies using AI in drug discovery?

The traditional process of finding medicines is complex, lengthy, and expensive. The time taken for developing a new drug varies from 10-14 years with an investment of about 3-4 billion USD. This time and cost estimation covers all the phases from research to manufacturing of a drug. However, by using the true potentials of the various resources of AWS cloud, and the state-of-the-art artificial intelligence algorithms and techniques, we can significantly reduce the time and cost to an extent. Some of the AI aspects that contribute to quicken the drug discovery process are:

• Data analytics capabilities of AI in drug discovery let researchers to swiftly navigate through loads of data points that can help gauge the target molecules’ capabilities.
• AI in drug discovery is expediting the analysis of massive medical data sets, generate data-driven insights, and predict future needs.
• Predictive analytics capabilities of AI contribute to identify and develop accurate chemical compositions, speed up trials and decide which new drug to manufacture.
• AI is contributing to manufacture good quality medicinal products, filter and employ best-matched patients for clinical trials and enhance the protection and efficiency of drugs during clinical trials.

All these aspects are accelerating the whole process of drug research and development making new drugs available in market at less cost and in shorter time. That's why pharma companies are increasingly using AI in their drug discovery and development programs.

As per a recent survey the global artificial intelligence (AI) in drug discovery market is expected to grow at a compound annual growth rate (CAGR) of 31.6%. Some of the major reasons leading to the increased use of AI in drug discovery are detailed below.

1. Novel Drug Discovery

Discovering a novel drug includes several batches of design, chemical combinations, testing, and clinical trials. Screening huge sets of molecules to identify the correct one having the potential to be a drug takes a lot of time. AI in drug discovery helps researchers to quickly navigate through billions of chemical and molecular compounds to find a potential target.

The drug discovery cycle includes various stages, and the initial stages of this cycle are the most time and resources consuming. AI can be used to make these stages more efficient and rapid and given below are some of the drug discovery stages which AI can expedite:

Target identification. This stage includes finding role of the best suited therapeutic target (gene/protein) in a disease. It follows a trial-and-error functional method.
Drug molecule design. An inventive process of designing a new medicine as per the knowledge of the identified target.
Chemical combination. A huge number of chemical compounds are quickly produced on a small scale in small reaction cells.
Drug screening. In this stage, potential medicines are identified and optimized before filtering candidates for clinical trials.
Clinical Trials. A research study performed in people to evaluate a medical intervention. This is a primary testing way to find out if a particular new drug is safe and effective in people.  

2. Optimize Drug Manufacturing

Pharma companies are using artificial intelligence to optimize the manufacturing of drugs. This is done by managing waste or supply chains with the help of AI-enabled predictive analysis.

3. Predict Precise Drug Composition

AI in drug discovery contributes to predicting the finest combinations of chemicals for a drug composition with its possible outcome. Using AI in drug discovery allows researchers try several different chemical compositions and explore their possibilities for being a drug.

4. Faster Clinical Trials 

The predictive analytics capabilities of AI in drug discovery help researchers filter the right patients to participate in clinical trials. Artificial intelligence selects the best candidate for the trial based on patient’s medical records, demographic information, or ethnicity. To generate valuable insights, AI also contributes by monitoring and collecting data about the drug's effects on a patient.

How is Cloud Computing Accelerating Drug Discovery?

Virtual screening plays a vital role in the process of drug discovery. But the practical application of virtual screening is expensive as it needs the use of computational servers, graphical systems, and costly modeling software platforms. This is where cloud computing can help to build a cost-effective solution. Cloud computing is budget-friendly, elastic, and fully managed by the provider. Therefore, it will be economical to use cloud as a substitute to these massive internal resources required for using virtual screening.

Today, many leading cloud providers in the market are offering healthcare exclusive services such as CentralReach, Amazon Comprehend medical, NextGen Healthcare HER, etc. AWS offers a whole range of services for medical data processing, analysis, and storage. These services help to extract and understand unstructured medical data. You can store, transform, or query the health data in minutes. With these features, researchers can quickly generate insights that can be further used in drug discovery and development. 

Pharma companies are using cloud-based services on a large scale to:

• Extract important health information from the unstructured medical text such as diagnoses, prescriptions, and reports.
• Determine relationships between extracted health information
• Relate the information to medical ontologies like ICD-10-CM, RxNorm, and SNOMED CT
• Automate and lower the cost of processing unstructured medical text
• Analyse and make predictions with extracted medical data
• Create a chronological view of patient’s medical data and much more.

Overall, medicine companies are optimising the cloud to cut down cost and time needed for launching new drugs to the market. 

Conclusion

Artificial Intelligence is accelerating drug discovery and development by contributing to the faster processing of immense data, better drug design, impelling modernization, enhancing clinical trials, and supervising drug management. This will eventually reduce the new drug’s cost and time to market. Just like any other sector, healthcare and life sciences are also exploring the plethora of benefits that cloud computing offers. 

Leveraging the cloud and AI in drug discovery is helping to turn impracticality into reality. However, many organizations are still skeptical about using these technologies. Some of the major challenges in adopting artificial intelligence by pharmaceutical industry are a lack of substantial amounts of data or budget for small companies, lack of skilled staff that can operate cloud and AI-based platforms, etc. These challenges can be solved with the help of industry experts and service providers who can help you design the right AI or cloud strategy in line with your business goals that too within your budget. Looking at the value adds that AI is capable of, ignoring to adopt it will only delay the huge benefits that AI can bring to your business. 

Do you want to explore the immense possibilities that AI can bring to your business? Contact us today to learn more about how we can help your company. 

Book a 1-hour free consultation with pricing details. 

Technology has revolutionized the Healthcare and Life Sciences industry. This has led to unprecedented ways in which the quality of human life has been enhanced: whether it be through early detection of diseases, steadfast treatments, rapid drug discoveries, or genome studies; many short-term and chronic diseases have found their magical potion aka cure.

Minfy has a history of expertise in the Healthcare and Life Sciences industries and continues to work with our customers to unlock various novel insights, find patterns from a plethora of human body parameters, and identify abnormalities from medical images. Drawing insights from such complex data can radically transform the preventive healthcare industry. The need of the hour is a place where all this structured, semi-structured, and unstructured data can be organized, analyzed, managed, and used for feeding the machine learning models for predictions.

This is where Amazon HealthLake comes in, and Minfy is excited to announce that it has been selected as an Amazon HealthLake Launch Partner for the Amazon HealthLake Partner Program. This service allows healthcare providers, health insurance companies, and pharmaceutical companies to store, transform, query, analyze, and share health data in the cloud. Amazon HealthLake offers various features for storing, transforming, searching, and analyzing data, saving both time and energy while providing smooth efficiency and high accuracy.

Some of the vital features of Amazon HealthLake and its potential benefits for the Healthcare industry are mentioned below.

Secure and compliant data storage:

Amazon HealthLake supports data in the Fast Healthcare Interoperability Resources (FHIR R4) industry standard. With the use of HealthLake import API, importing files from S3 storage to the HealthLake datastore including lab reports, clinical notes, and insurance claims, is quick and easy. Amazon HealthLake’s data store creates a complete view of each patient’s medical history in chronological order and helps index all information to easily search without any hassle.

Amazon HealthLake is a HIPAA eligible service that allows for encryption with customer-managed keys. Customers can rest assured knowing that private health information (PHI) can be encrypted at the datastore level. You can control user access based on user needs with column level security to power AI/ML analytics.

Meaningful data transformation:

Integrated medical natural language processing (NLP) transforms all raw medical text data in the datastore to discern and extract meaningful information from unstructured healthcare data. Integrated medical NLP, which Amazon HealthLake leverages via Comprehend Medical, can automatically extract entities (e.g., medical procedures, medications), entity relationships (e.g., a medication and its dosage), entity traits (e.g., positive or negative test result, time of procedure), and Protected Health Information (PHI) data from medical text.

This transformed data is comparatively more mature and readier to be used to capture a patient’s medical history. What previously took hours or even days to capture and analyze can be done within minutes.

Better decision support by better search capabilities:

Amazon HealthLake supports FHIR Search and CRUD operations. In other words, the users can create, read, update, and delete pieces of information for more comprehensive and rich datasets. Even the electronic health/medical records (EHR or EMR) can be indexed into Amazon Kendra for an accurate representation of the medical history of the patient by ranking the content using Amazon Neptune knowledge graphs. With this semantic search, the query will fetch data that have context, unlike any other lexical search.

As the search results improve, the efficiency of comparisons between medical records and notes increases. When the medical records are well organized, then the shared clinical characteristics with the use of Neptune allow users to view metadata associated with patient notes in a simpler and standard view. This early intervention and efficiency of healthcare providers give more time to consult with patients better and allows a comprehensive check with a proper prescription to sustain a healthy life and hence move towards preventive care.

Detailed analysis and more promising predictions:

It is worth noting that Amazon HealthLake can seamlessly leverage Amazon SageMaker to build machine learning or deep learning models that can solve healthcare and life sciences challenges by interpreting the results via visualization techniques. Amazon HealthLake also connects to Amazon QuickSight to create dashboards by exporting and normalizing data to quickly explore patient trends. This can help find better insights, patterns, and anomalies in the health data. This will further enable regular interventions by healthcare providers to prevent a particular disease in its early stages itself.

Amazon HealthLake’s streamlining of data analytics and AI/ML model capability accelerate data insights for healthcare industry stakeholders. Several AWS cloud services can be used in conjunction with Amazon HealthLake, including Amazon Sagemaker to train the ML/DL models, Amazon QuickSight for visualization, Amazon Polly for telehealth, Amazon Kendra to search using natural language, and last but not least Amazon Rekognition to understand the medical images better, and these services can all be integrated to make the complete healthcare system exponentially efficient. Minfy is ready to leverage these services to deliver results to our customers and solve Healthcare and Life Sciences’ most challenging problems.

Businesses are embracing app modernization on a vast scale. The reason can be to meet greenfield necessities, make business future-ready, or to upgrade monolithic legacy applications. On their journey to modernization, businesses are using containers and Kubernetes as primary technologies to modernize the design and distribution of their applications. The key business goal remains the same, which is to have an all-time-available work system in place. A system that is scalable, portable, flexible, and reliable. Architecture based on microservices and Kubernetes, and the 12 factor app methodology can help achieve such a system.

The 12-factor app style of development surfaced about 10 years ago, much before containers. And, since then the 12 principles of the 12 factor app have become a universal standard for cloud-native app development. The 12 factor app development stages offer a set of guidelines for a proper outline for developing modern microservices. And, Kubernetes is known for being an orchestration platform for containers used to deploy and control these microservices.

The 12-factor app principles:

• Has only one aim: to offer a course of action for cloud-native application development and deployment. They ensure that happens by making applications highly scalable and disposable.
• Help you and your team to embrace DevOps and microservices in the app development process.
• Simplify the process, which increases the development time and reduces the time to market.
• Were designed to build Software as a Service (SaaS) applications by alleviating the difficulties associated with long-term software development.

This article explains how organizations are leveraging the 12-factor app development method and Kubernetes to architect cloud-native apps. Understand how 12 factor app is helping businesses to modernize by establishing scalability, resiliency, robustness, mobility, and reliability across their applications. Let’s get started.

Leveraging 12 Factor App Principles and Kubernetes

1. A single Codebase for Applications, Multiple Deployments

A 12-factor app methodology states that only one Codebase or a set of code repositories should exist. These are deployable multiple times but never have many codebases. If there are any shared codes, they should be factored into libraries and called through the dependency manager.

Multiple deployments of a codebase are possible by making it active across all instances. The only difference is the versions, which are also tracked in the version manager.

Once the code base is in place with the 12-factor app approach, it can be built, released, or run in separate phases in the Kubernetes environment. Kubernetes and containers have text-based representations. The predictable system states are managed by automation tools in separate files. It is better to manage such evolving artifacts with source control. Using a version control system such as Git can help eliminate the introduction of sudden changes and facilitate tracking the changes added to your system.

2. Declare and Isolate Application Dependencies

The 12-factor app methodology uses the declaration and isolation method for application dependencies. Declare any dependencies explicitly and also check them in the version manager. This approach makes it easier to get started and enhances repeatability. It also becomes easy to track any changes made to the dependencies.

Another approach is to package the app and all its dependencies into a container. This makes it possible to remove the app and all its dependencies from its environment. In addition, it ensures that the app functions as expected regardless of the differences in development and staging environments.

3. Archive Config as Environment Variable

As per 12 factor app principle configs should be archived as environment variables (env vars) but not constants. Env vars are easy to change as the need arises for new code deployments without changing the codes. This flexibility quickens the native-app development process.

Additionally, you can manage env vars independently every time you deploy them. It also becomes easy to scale up as the development process progresses towards completion and deployment.

The 12-factor app strictly separates the application configuration from the code. Kubernetes ConfigMap supports storing configuration by declaring it. This can be helpful for production and development environments that need different configurations to deploy the same code.

4. Backing Services: As Attached Resources, Easy to Swap

Backing services include support applications and systems that your application needs to connect and communicate with, such as databases. They are usually grouped as attached resources that should be accessible when needed.

Modern applications that are microservices-based use backing services. These backing services are handled as attached resources in the 12-factor app. Due to this, in case of any failure, you can simply change the attached resources and not the whole application codebase.

Backing services in the 12-factor app methodology are configurable and easy to change. You can change them from one state to the next as the need arises. The switching is possible by just slightly changing the configuration.

It is the best practice to separate the backing services (such as logging, messaging, databases, third-party services, caching, and others) from the system. And then interact with them through an API. Sticking the APIs to consistent contracts will let you change the basic implementations without highlighting them to clients. Kubernetes ConfigMaps can be used to store connection information to skip building the container image again, in case of any revision in the connection information.

5. Split Build, Release and Run Phases

The 12-factor app methodology distinguishes all the stages of cloud-native app development.

• They changed the codebase to deploy it. And once the first stage is completed and the next starts, you cannot alter the code in the previous one.
• You should build deployable components independent of the environment in the first stage. The second stage involves taking the reusable components already developed and combining them with a specific configuration to match the target environment.
• The last phase is the run stage. It involves packing the entity created in the previous one in a container and running it in the target environment.

Organizations prefer to automate the development and testing tasks with CI/CD toolchains. Splitting your CI/CD pipeline into a series of sequential tasks can increase productivity. It helps to provide greater insight into failure and improve accountability. For example, dedicate a pipeline exclusively for building a container image at a time. After that, to run the container instance, you can perform the testing, promoting, and deploying of that image.

6. Stateless Processes

The 12-factor app methodology allows you to run cloud-native applications in the environment as one or more processes. The only restrictions are that they should be stateless and never share data. That enhances scalability and portability across cloud computing infrastructure. Data compilation is done during the build stage. Any other thing that requires persistence forms part of the backing services.

Containers are short-lived and when the container goes away, the data inside the container ceases to exist. The state of containerized workloads must be reduced. This helps to maintain a good user experience while remaining unaffected by application scaling.

7. Port Binding to Export Services

This stage of 12-factor app development involves binding your packaged application to a port. You can use the Kubernetes service object if the workload is exposed internally to the cluster. Otherwise, you need other methods such as node ports, Ingres Controllers, and OpenShift routes.

Packing your application inside containers makes networking and port collisions easier by reducing the workload on hosts. Software-defined networks in Kubernetes platforms take over many operations.

8. Concurrency and Scalability

Scalability is one of the primary features of any cloud-native application. That is usually done by deploying more app copies instead of enlarging them. To achieve this, the 12 factor app methodology uses a simple yet reliable operation.

The developer designs the app to take on different workloads by assigning processes varying tasks. An example is an application where a web process handles HTTP requests and a worker processes a long-running background activity.

The pod-based Kubernetes architecture supports the scaling of application components as per varying demands. With the 12-factor app’s stateless processes element, scalability becomes a consistent function that can help to gain an expected level of concurrency.

9. Disposability: Robust Cloud-native apps

According to the 12-factor app methodology, all processes are disposable. They should have minimal startup time, shutdown gracefully, and be immune to crashes and failures. All these capabilities make scaling easier, enhance faster app development, and make the deployments more robust.

The app should create new instances when it needs to and take them down as necessary. It is this 'disposability' property that makes cloud-native applications more robust. In microservices, processes are disposable. That means, in case any application stops working unexpectedly, users stay almost unaffected and the failures are managed gracefully. You can also use Kubernetes ReplicaSets to uphold the stage of availability for microservices by specifying the max-to-min bounds for the number of replicas.

10. Dev/prod parity: Carrying out Development and Production Similarly

The 12-factor app methodology bridges the gap between cloud-native app development and production. That makes it possible to continuously deploy or roll out new features. Also, developers can write code, deploy, and review the app’s features. This process is usually fast, and completed in minutes or hours.

For organizations that pack workloads into containers, initializing the container image in one environment, it shall run on any infrastructure or environment. But there are chances of environmental drift. Consider standardizing the same distribution of Kubernetes across all environments to eliminate this. It helps create a consistent experience for container platform users.

11. Logs are Event Streams

The 12-factor app methodology does not require routing and storage, writing, or managing of the application’s output or log files. Any running process writes its event stream to STDOUT without buffering. A developer views this stream in the foreground of their user interface. This helps to determine how the app behaves and draw conclusions. The event streams also make it easy to troubleshoot or debug an application.

12. Run Admin Processes as One-off Processes

With a 12-factor app methodology, admin and management tasks such as database migrations, scripts, and batch programs run as one-off processes. They are treated as long-running processes. They also utilize the same dependency isolation methods as the app’s usual processes.

It is best practice to isolate the application administrative tasks such as data restore and backup, caching, or migration from the application microservices and carry them out as separate processes. You can use Kubernetes jobs to execute these mundane administrative tasks that are part of the application lifecycle.

What are the Business Benefits of 12 Factor App Methodology

The 12-factor app methodology is the how-to guide for creating cloud-native applications. Many giant tech companies, such as Amazon, Heroku, Microsoft, and others, make use of these 12 principles as they technically help them to enhance business agility by expediting innovation and go-to-market capabilities. With these 12-factor principles, you can design and maintain a robust and modern app architecture required for cloud-based applications.

This methodology is the solution for developers developing the following:

Software-as-a-service solutions

Cloud Applications

Distributed software solutions

Microservices

With the 12-factor apps methodology, you can create cloud-native applications that are:

• Suitable for deployment on modern cloud platforms, minimizing the need for servers and server administration
• Enabled for continuous deployment with minimal differences between development and production
• Scalable without significant change or effort
• Capable of using declarative formats for setup automation.

Conclusion

Web applications, platforms, and frameworks using the 12-factor app methodology have generated measurable business outcomes with enhanced productivity in the past few years. This guidebook is suitable for DevOps and cloud app development, which should be a blueprint for developing resilient, scalable, portable, and maintainable applications. Considering these 12-factor app principles with Kubernetes ensures you build a robust solution for your business.

However, this methodology is not the ultimate solution for everyone. Whether or not it works for your business depends on your business model and needs. So, you should not worry if your software development process deviates from the principles of 12-factor app methodology. You are good to go if you understand the reason and the expected outcome.

Do you want to learn more about 12-factor app development principles and its real-time use cases? Contact us today to know how we can help your company.

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The world's life expectancy has increased as a result of various advancements in healthcare facilities. However, as the world's population ages, healthcare systems must contend with a rise in demand for their services, growing costs, and a staff that is under pressure to meet patient needs.

With the growing population of senior citizens and the adverse effects of climate change, there will be a huge rise in health-related issues. It will lead to a higher influx of patients with various needs to several healthcare institutions. To tackle this meteorically rising need and the economy behind it, healthcare systems need to transform their service deliveries from episodic to management-focused long-term care.

But how can healthcare systems achieve it effectively?

This is where healthcare AI solutions come into play. Artificial intelligence (AI) solutions have the potential to handle these challenges and empower the healthcare industry to be future-ready. By introducing new innovative ways of tackling operational challenges, AI solutions are and will be transforming the healthcare industry in 2022 and beyond.

However, before moving on to discuss various AI solutions in healthcare, let's first understand how AI solutions sync with the definition of healthcare systems

What are AI Solutions in Healthcare?

It is quite known that artificial intelligence (AI) is the capacity of computers and other machines to think, learn, and behave very similarly to humans. The employment of cognitive approaches like AI algorithms in medical situations is referred to as artificial intelligence (AI) solutions in healthcare. Very often to predict certain medical outcomes, AI solutions in healthcare are used to build quick response and effective analysis platforms against huge chunks of medical data.

Industry Application of AI Solutions in Healthcare

The healthcare industry is already using AI solutions for better clinical decisions and faster operations. In healthcare and life-science companies, AI solutions offer several advantages over traditional analytical methods. AI solutions can make healthcare systems more precise with the ability to understand training data, which further helps humans get unprecedented insights into treatment variability, care processes, diagnostics, and patient results.

Over the last few years, AI solutions have significantly contributed to the healthcare industry. From providing better service to patients to performing advanced analysis and treatment AI solutions are fueling healthcare institutions to achieve better growth and revenue. Now, let’s take a closer look at 5 healthcare AI solutions that are transforming the healthcare industry as a whole.

1. AI & Machine Learning (ML) Solutions for Medical Imaging & Diagnosis

"Medical imaging" refers to a variety of methods that are used to examine the human body to discover, monitor, or treat several disorders. Leveraging AI solutions, medical imaging and diagnosis can be done with better precision and accuracy. Unlike humans, AI solutions can help in better recognition of intricate patterns of various human body ailments. Few real-world applications of this can be:
• The algorithms at the core of the AI solutions can expedite the process of reading complex images from CT scans or MRIs.
• AI solutions-based Machine Learning (ML) platforms in healthcare are helping to analyze MRI a shorter timeframe right from the symptom onset. It finally helps in detecting many life-threatening diseases at an early stage. For example, a brain MRI analysis with AI solutions based Machine Learning (ML) platforms helps identify tissue changes reflective of early ischemic stroke.
• Doctors can perform better and more accurate disease diagnoses with the help of automated image diagnosis systems.

Using these AI solutions in your healthcare institutions, you can directly boost healthcare expert’s productivity. Additionally, using these AI solutions in healthcare, you can ensure that your highly skilled staff spends time treating more patients rather than evaluating medical pictures.

2. AI Solutions for Next-Gen Patient Screening

AI can be helpful in pre-screening patients long before they arrive at a hospital.
• Instead of sticking to conventional pre-screening questionnaires, healthcare institutions need to enable AI solutions-based speech and text-based interactions. It will lead to faster screening and better management of a higher volume of patients.
• Reinforcing ML with AI solutions will further help to identify novel trends and enhance patient complaint diagnosis. In comparison to a preset or static survey, AI solutions like chat-bots can easily replicate the expertise of a real-world healthcare practitioner.
• Adopting AI solutions-based Natural language processing (NLP) applications allows your potential patient to speak or write to a doctor about their symptoms. The AI agent and patients will quiz one another to mimic a conversation between a doctor and a patient.
• AI solutions can provide virtual health assistance to help advise a patient on how to proceed with their medical care. It includes timely reminders about check-ups, whether to visit the emergency room, schedule a consultation, or take an over-the-counter medication. If the patient needs medical attention, the AI solution can recommend a doctor based on the patient's location, availability, and insurance information.

In cases of health emergencies or growing accessibility issues in urban areas, AI solutions in healthcare can be a revolutionary innovation.

3. Predictive AI Solutions for Preventative Healthcare

Preventative care, as the name itself suggests, is any procedure or method that defends against any probable health issues. Today, the term "preventive care" solely refers to regular medical checks, immunizations, dental cleanings, etc. Leveraging predictive technology of AI solutions can significantly enhance ongoing preventative treatments and boost overall healthcare infrastructure.

• Smart wearables like the Apple Watch, Fitbit, Garmin, etc. work on the basis of AI solutions coupled with sensors. These smart fitness wearables can support preventative care in the healthcare sector.
• Information on heart rate, activity, nutrition, VO2, and sleep is automatically supported by these devices. With add-ons, these devices can now measure blood pressure, blood sugar, and weight as well.
• Preventative healthcare is possible by using the data from these wearables to identify potential health issues even before they occur. This information can be used to build analytical models that will help in making accurate predictions regarding certain illnesses.
• These models are able to gather data from wearable sensors in real-time, alerting users when they have reached a threshold calling for preventative medical care.

The gadget-based healthcare AI solutions help share data with the physician for a detailed medical understanding. Additionally, it can help offer better care by spending less time on the patient's symptoms and background.

4. AI Solutions for Healthcare Data Management

The healthcare industry has to deal with a huge pile of data. This increases the chances of straying imperative data. Big medical data comes with bigger data management challenges. It can take years to process this data, connect significant data points, develop an appropriate diagnosis, or support new medicines’ discovery out of this huge data.
• Data is an asset to every business, and hence, it should be of top priority to preserve it. AI is helping many healthcare organizations to realize important data points from health records’, analyze and then present it for easier examination and maintenance of patient records.
• AI solutions when deployed in healthcare, can lead to easier data processing and bring quick visibility into patterns, which can be very daunting for any individual.

5. AI Solutions for advanced Healthcare Drug Discovery

COVID-19 has shed light on the creation of vaccines and technologies like AI, ML, or DL have proved to be of greater help in expediting the vaccine discovery process. It has taken a year and more for the world to discover the vaccine. It has only shown that the processes for identifying vaccines and pharmaceuticals are labor-intensive, expensive, time-consuming, and usually unsuccessful.
• Pharmaceutical companies are increasingly using AI tools like DL to develop and test new drugs.
• It needs a lot of data analysis to develop new pharmaceuticals since there are so many possible chemical combinations. AI solutions being exceptionally faster and quicker at handling enormous data volumes can accelerate the entire process.
• AI solutions can help create more medicines that are authorized in less time and at cheaper costs.

AI solutions can help bolster clinical efforts toward faster drug discovery. This will lead to faster market release and subsequent cost & time savings.

Summary

• AI solutions in the healthcare sector can help every healthcare worker to view patient data and other information to provide accurate diagnoses and treatment suggestions.
• AI solutions let healthcare institutions gather huge data sets like clinical trial data, claims data, demographic data, etc. Deploying AI solutions can lead to faster data processing which will probably take years if done through the conventional methodology.
• Using Ai solutions in the healthcare sector can lead to the discovery of necessary data patterns and insights, thus, potential for further scalability.

The above, plus various other benefits of AI solutions, have made it a turning point in the healthcare field. Whether in the form of machine learning that aids in the development of medicine, patient prescreening, in-treatment communications through NLP or AI solutions like imaging analysis, AI solutions are already outgrowing the conventional methods and are bound to grow more in coming years. Explore how our AI solutions are modernizing our clients’ healthcare operations and helping them achieve big in their business. Do you want to know how AI solutions can benefit your healthcare company? Contact us today to learn more.

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Cloud computing necessitates substantial flexibility for businesses in order to reduce barriers to entry into a platform that is scalable and enables rapid innovative growth. The tough part begins when platform owners have to choose the type of migration procedure to use to move their platform to the cloud. Even a straightforward lift and shift depends on a lot of variables, and needs restructuring and planning.

There are many different approaches for platform migration to the cloud. The main objective is to give your business speed, agility, and the ability to experiment in an economical way, regardless of how the migration is addressed, whether it is through the refactoring method or down to the typical re-host technique. A migration may cause downtime or prevent your company from fully optimizing benefits of the cloud if it is poorly planned or executed.

While migrating to cloud, there are possibilities of encountering inconsistencies along the way, conflicts with different existing policies and the legacy or core services that are about to change. Therefore, migrations demand an in-depth, granular planning and cross-disciplinary awareness of platform minutiae, along with meticulous, detailed preparation, assistance and advisory from domain professionals (platform architects, operations experts, developer resources) with team collaboration and cooperation.

Today, many advanced technology teams are evolving their DevOps processes to address these inefficiencies, bridge the gaps between disciplines, and realign previously disparate interests. Let see how automation and DevOps are becoming viable goals on roadmap to cloud migration for businesses offering a great platform to pursue the most awaited cultural shift.

Why Adopt DevOps Culture

The use of a set of cultural concepts, practices, and technological tools collectively referred to as "DevOps" improves an organization's capacity to deliver applications and services at high velocity—that is, to change and improve products more quickly than with conventional software development and infrastructure management techniques. Due to this quickness, firms can better serve their customers and increase their marketability. Your ability to successfully migrate to the cloud may be greatly impacted by using a DevOps approach.

Furthermore, DevOps supports a flexible experimentation phase, adaptability, and an agile engineering culture. In DevOps, the iterative aspects such as develop, test, deploy, scale, failover, and recover are designed to be streamlined. The key DevOps concepts of shared accountability, agility, transparency, and minimal risks through automation have been shown to be essential for handling complicated projects like corporate platform migrations.

DevOps enables businesses to achieve change quickly by combining the heritage culture and practice with a set of paths. Ultimately, by successfully competing to the industry expectations, organizations can attain their business ambitions better and produce more significant ROI with DevOps.

Establishing a Mature DevOps Practice in Your Organization

Modern development teams immediately deploy each developer's changes to enormous environments many times a day. Deploying more frequently and in smaller increments can reduce significant risk during deployment. Lower change deltas reduce the time it takes to find and resolve issues, which is further reduced by rapid feedback loops enabled by extensive automated testing of each change increment.

Companies were able to perform more tests at a lower cost and iterate more quickly as a result of the use of automation by DevOps teams to move their infrastructure and workload to the cloud. Here is a list of elements that support developing a mature DevOps practice inside your organization to assure continuous improvement to your software and services and meet your business goals.

1. Encourage a collaborative culture

The successful collaboration between the operations and development teams is the first and most crucial tenet of DevOps. The DevOps team can concentrate on achieving the organization's common goals and serve its purpose by forming a unilateral team. The main idea behind this notion is to make sure that the team works well together and communicates with one another so that they can exchange ideas and work through challenges.

In this approach, silos can be broken down and people, processes, and technology can all work together to achieve corporate goals. Such specialized teams can generate a fluid experience through process alignment, which can support a culture of innovations and deployments throughout the whole business.

2. Automate as much as you can

The necessity of the era is automation. Continuous development is necessary to satisfy client needs as consumer awareness grows. With this in mind, significant progress has been made in terms of implementing technologies that assist automation as well as optimizing other processes, such as configuration management, the CI/CD pipeline, and others.

Several procedures can be automated during the automation phase to operate effectively with the available resources. This may entail setting up new systems, developing new infrastructure, deploying software, and running numerous tests for functionality and security compliance.

DevOps enable teams to create their own automated procedures and expedite development and deployment processes. In addition, while maintaining their dependability, machines can be effectively trained to distribute software more quickly than they often do.

3. Make data- driven decisions

Success is the result of making wise choices over time. By always pursuing alignment with product and technology objectives and vice versa; business strategy and financial planning cycles must adjust to the reality of constantly changing markets. This calls for a more thorough, data-driven decision-making process at all levels.

4. Utilize Infrastructure-as-a-code strategy

Every development effort should be cloud-native, use an infrastructure-as-a-code methodology, use tools like AWS CloudFormation, and reduce reliance on manual processes by utilizing the appropriate public, private, and/or hybrid cloud infrastructure. With this method, you can configure, manage, grow, and recover from problems while eliminating manual labor and human error. Eventually, the entire infrastructure lifecycle will be totally automated.

5. Encourage tool-driven teamwork

Deep, effective, tool-driven teamwork amongst knowledgeable, cross-functional persons working as members of a team that carry out a product development and operations lifecycle is a trait of mature DevOps teams.

6. Adopt a customer first approach

Brands that don't continually innovate cannot satisfy the requirements of the consumer. For the same reason, brands must act like lean start-ups that innovate as the times change. Continuous improvement, which focuses on performance, speed, and cost of delivery optimization, is a key component of DevOps. Adopting DevOps necessitates a strong organizational commitment to ongoing improvement. Continual experimentation combined with a strong dedication to ongoing learning results in continuous progress. The effective business will discover how to ask questions and reduce the likelihood of making incorrect assumptions.

To satisfy their customers' needs, DevOps teams are always on the go. The information acquired through automated procedures can be examined to see whether performance goals are being fulfilled, which will eventually result in customer satisfaction.

7. Establish a culture to support consolidated responsibility

Traditionally, developers and operations teams used to operate independently with little to no communication. On the contrary, the DevOps environment compels both the teams to collaborate in order to accomplish shared objectives and function as a single team. But now that the DevOps culture has changed, this idea is approached differently. A space for everyone and everything is provided inside the teams, and inefficiencies are eliminated.

Migrating to Cloud with DevOps Culture

Early adoption and adherence to DevOps methods allow for low-risk migration implementation. DevOps offers a range of immediate and long-term business benefits, including repeatability, accuracy, much fewer deployment risks, and shorter iteration cycles - all of which are critical for employee engagement, operational efficiency, security architecture, and competitive advantage.

Here are few best practices that helped organization to successfully implement the cultural changes required to adopt DevOps when migrating to cloud.

Best Practices to Inculcate DevOps Cultural Shift When Migrating to Cloud

1. Emphasizing the hiring process

DevOps, at its heart, unites development and operations teams that have been divided for a while due to your outdated software development life cycle (SDLC). To ensure that employees collaborate and effectively provide solutions to the market, DevOps mandates that enterprises change their strategy.

Reviewing the selection criteria for new employees is one method to guarantee a focus on people and culture. Since generalists are better able to manage the constantly changing demands of development, DevOps teams place more emphasis on employing them. As DevOps approaches grow, generalists will need to embrace a software development perspective.

Organizations can opt for hiring generalists who are open to adopt a software developer’s mindset easily.

2. Sharing accountability

Development teams have been plagued with application outages all the time. DevOps teams are further motivated to incorporate methods that guarantee continuity by the pressure to please customers and ensure application uptime.

Set high priority on delivering workloads and applications that are extremely robust, and then hold the company responsible. Teams are more inclined to collaborate and tempers are less likely to flare when simple KPIs like Mean Time to Production, Deployment Frequency, and Mean Time to Recovery are established.

3. Considering learning and training as an ongoing activity

Many SDLC procedures are made clearer by DevOps. Simple operations become programmed and flexible. This increases operations' predictability, effectiveness, security, and sustainability to the fullest. Automation frequently serves as assistant for these procedures and methods. Make continual training a part of your DevOps adoption plan to establish the expectation of continual improvement.

Leaders need to give the necessary tools so that their staff can stay up to date with the rest of the industry when new technology and best practices are developed. This covers actions like earning certifications and gaining practical knowledge of certain DevOps technologies.

4. Working together towards a single objective

The mechanics of cooperation in a DevOps system is another topic of research. DevOps breaks down silos and forces stakeholders from various business divisions to collaborate, therefore it promotes empathy and mutual understanding as fundamental team principles that help people work together toward a single objective. To encourage communication and collaboration among team members, organizations can find it advantageous to participate in team-building exercises.

Final Takeaway

• The DevOps ecosystem consists of a diverse range of frameworks, technologies, and cultural elements.
• However, as history has shown, processes change through time and tools are always being changed. What will endure are the cultural shifts that fuel the adaptability required to change along with the ecology.
• In order to support self-organizing teams based on products and applications, organizational adjustments must be undertaken, with leadership encouraging and facilitating cooperation. DevOps is fundamentally about empowerment and autonomy.
• Many migration projects have generated measurable and anticipated outcomes with continuous testing, complete automation, and the application of modern software development practices of DevOps.
• The goal of producing software more regularly, with less work, and fewer errors has been pursued by developers for many years. Finally, we have the methods and tools needed to make this a reality.
• As per a survey, organizations practicing DevOps generates a higher quality deliverables with greater deployment frequency and faster time to market. Hence, despite its challenges developing a DevOps is worthwhile, due to the increased satisfaction it offers to the developers, managers, and customers.

The cloud migration phase offers a big opportunity for organizations to improve processes, applications, culture and move towards adopting DevOps. It gives a chance to change something that is not beneficial anymore, stagnant and unnecessary. If your organization is going through the same phase, feel free to try the DevOps possibilities to bring that necessary change in your organization and witness the amazing benefits that come with it.

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Moving from an on-premise IT system to cloud-based services can be a daunting task. This transformation can result in security challenges and other problems. However, businesses have to embrace this new technology for the benefits that come with it.

Adopting cloud-based IT infrastructure without proper cloud governance in an impromptu manner may lead to more drawbacks than benefits. Cloud grants agility and new speed to innovation like never before by making it easier for teams to deploy assets or provision infrastructures at a press of a button. But these benefits of the cloud come at the cost of new risks that cloud governance can help to diminish. As cloud services can burn a hole in your pocket, it is imperative to set up well-organized cloud governance that considers security and compliance, data management, company assets, configurations, applications performance, budget, operations and much more to enable optimal use of the cloud for an organization in a dependable and secured way. That is why a business must consider cloud governance.

Proper implementation of cloud governance helps in risk management, cost optimization, productivity improvement, and enhanced data security. Read on to understand what cloud governance is and why your business needs it.

What is Cloud Governance?

Cloud governance consists of rules and protocols devised by organizations and businesses to help improve data security and risk management. These rules ensure all operations run smoothly and allow employees to use and deploy new cloud systems more conveniently.

Cloud governance also makes the transformation from on-premise IT infrastructure to cloud flawless by considering all factors. All employees also have seamless access to cloud infrastructure and systems. The approach additionally encourages collaboration where necessary. That’s why it is paramount to include cloud governance in your business.

7 Reasons your Business needs Cloud Governance

You may need extra time to design and implement the best cloud governance. But if you put in the required efforts, you can get many benefits. These are the reasons why your business needs cloud governance. Let’s explore just a few of them.

1. Cloud Governance Helps Regulate Accessibility

IT cloud infrastructure is remotely accessible to all employees in an organization. Uncontrolled access to data can cause data integrity and security issues. Security can be a concern for cloud-based IT systems without rules and protocols. That is why cloud governance is a necessity to restrict accessibility.

Cloud governance allows employees to only access the section of the system they are permitted to, which also applies to cloud applications. That way, dishonest and rogue applications can never interfere with sensitive architecture. Cloud governance makes it possible to decide who has access to what and how the data acquired is used.

Additionally, you can regulate data and monitor activities in real-time. So, any data breach is timely reported to the system administrator, and necessary actions are taken on time.

2. Enhances System Security

Data security is another concern when migrating to the cloud. It can increase some risks to the system and operations, such as data storage, system maintenance, and others. Cloud governance is here to help minimize these risks and make the cloud system more secure.

Putting protocols in place ensures proper data management in the cloud. Data integrity and confidentiality can be maintained with well-laid out rules. It also enhances visibility and control over who accesses what information and outlines how that information is used.

3. Slashes Operational Costs

You no longer have to hire IT specialists to track accounts, check compliance, and analyze operational costs the traditional way. Cloud computing has made it possible to automate everything, including controlled access and policies.

You can reduce operational costs by preventing non-compliant activities with proper cloud governance. Additionally, you can keep shadow IT in check, eliminate unnecessary expenses in infrastructure. It is the best way to keep any project under budget and only carry out the ones vital for the operation of your business. Setting the right cloud governance enables organizations to a kind of creating automated guardrails that will take care of all cloud aspects from budgets to policies management. You get automated responses for any cloud activity that happens, this lessens the load of pulling analytics for each and every cloud action, which in turn adds to your company’s cost saving by reducing the need of workforce.

4. Reduces Compliance Risks

Security compliance audits and assessments have become mandatory for all businesses. Your business operations and security system can be out of order if you don't have cloud governance. Moreover, not many organizations are unaware even data and security compliance exist.

Cloud governance helps businesses to make a long list of security compliances. That will ensure you always comply and avoid hefty fines that regulators impose on noncompliance. It will also help you audit your data policies and usage from time to time. Your employees will always know how to access and use data collected from your clients.

5. Easier Resource Management with Cloud Governance

The main reason for using cloud governance for your business is to better manage the cloud resources. It helps to seamlessly offer accurate control without breaching any security protocols.

With proper cloud governance, your business or organization can manage accounts and grant access to users accordingly. This approach enhances data security and helps to organize and maintain cloud resource allocations. You can group them into departments, projects, cost centres, and other categories relevant to your business.

Proper cloud resource management aids in better cost tracking and addresses most security concerns. There is also controlled access to those cloud resources.

6. Promotes Teamwork

Cloud governance avails resources to team members, making them feel motivated to handle the tasks. Besides, most of these processes are automated. So, employees will have more time to do what they like doing the most. The result is improved productivity in the team and organization overall.

7. Diminishes Shadow IT

Shadow IT systems involve any IT resources not defined within the IT departments. It becomes difficult to track them and determine their costs because they are often not captured in the reports. Shadow IT can happen to any business or organization regardless of its structure.

If your employees cannot access the cloud resources or don’t know how to find them, the chances and risks of shadow IT become high. That can increase security risks and inflate management costs. Cloud governance offers the best approach to prevent that from occurring. It details protocols to be followed to request and access resources without compromising other system operations.

Conclusion

Cloud governance is helpful to businesses of all sizes. It can curb shadow IT, reduce risks, enhance data security, improve cloud resource management, and provide access control. You should consider implementing cloud governance if you don’t have it yet.

However, you must understand you will never find a universal solution for cloud governance. The implementation process and management depend on your organization’s needs and structure. Also, you need to continually adjust your cloud governance to ensure policies and new rules are updated.

Till date, we have helped many organizations in creating a comprehensive cloud governance mechanism and achieve improved data security, secured data accessibility, better resource management and more cost savings. If you have cloud governance on mind, talk to Minfy’s cloud experts.

Book 1-hour free consultation with pricing details

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