Blog | Better Together. Better World.

AWS announces Minfy as Amazon Healthlake Partner. Better Together. Better World.

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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.

Blog | Kubernetes to Architect Cloud-Native Apps.

Leveraging 12 Factor App Principles and Kubernetes to Architect Cloud-Native Apps

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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




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.


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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Blog | 5 AI Solutions Transforming Healthcare in 2022 and beyond

5 AI Solutions Transforming Healthcare in 2022 and beyond

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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.


• 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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Blog | Adopting DevOps Culture When Migrating to Cloud

Adopting DevOps Culture When Migrating to Cloud

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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.

Do you have DevOps questions in mind? Contact us today and learn more about how we can help your company.

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