Table of Contents

Azure DevOps for Data Science

Defining DevOps

DevOps integrates software development (Dev) and IT operations (Ops) practices. It has the vision of providing continuous delivery with high software quality and shortening the systems development life cycle. DevOps is helping organizations to make agile software development.  

In other words, DevOps is the sum of people, processes, and technology to provide value to the customers continually.  

Parts of DevOps

Following are the bricks which construct the giant skyscraper known as DevOps: 

  • Code: It is the essential and first step in the DevOps life cycle; developers build the code on the platform of their choice.  
  • Build: Developers build the version of the program according to them, depending upon the language they are using.  
  • Test: The testing process must be automated using automation tools for DevOps to succeed.  
  • Release: It is a process of managing, scheduling, planning, and controlling the build in different environments after testing.  
  • Deploy: All the application files are ready, and execution play on the server.  
  • Monitor: This phase helps in providing essential information that helps to ensure service uptime and optimal performance.  
  • Plan: This stage collects information from the monitoring stage and implements the changes for better performance according to the feedback.  

What is Data Science? 

Data Science depends upon data availability, whereas business analytics does not entirely depend on data.  

Data science covers some parts of data analytics, particularly those containing programming, complex mathematics, and statistical.  
It can improve prediction accuracy, depending on data extracted from the various activities.  

Data Science Lifecycle 

Data Science Lifecycle centralizes the application of machine learning and different analytical strategies to produce predictions and insights from information to acquire a commercial objective. This complete method involves data cleaning, modelling, preparation, evaluation, etc. It is a lengthy process and can take several months to complete. So, it is essential to have a generic structure to observe for every hassle.  

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Why Data Scientists and DevOps needs to know each other? 

Data scientists create value by experimenting with new ways of modelling, transforming data and combining data. Meanwhile, organizations that support data scientists motivate themselves for stability.  

Data scientists, developers and the engineers who implement their work have entirely different tools, skillsets and constraints.  

The DevOps community helps break the wall of confusion between Development and Operations. We see this hurdle of confusion also applies in the data science world. Data scientists focus mainly on advanced analytics models.  

The DevOps Agile Skills Association (DASA) defines six principles that should adopt by an organization that wants to adopt DevOps. These principles are:

  • Joining the knowledge gap in the deployment process 
  • Infrastructure Provisioning  
  • Iterative Developments  
  • Scalability 
  • Monitoring 
  • Containerization

If you are interested in Azure DevOps for data science and the role both the branches share, feel free to contact us at:


Organizations isolated lab settings for their data science limitations need to replace a professional Data and Analytics domain in integration with mature business and product teams that adopt data science capabilities. The Data and Analytics domain will provide user-friendly self-services for Data and insights creation. 

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