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DevOps with Machine Learning
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How to boost DevOps with Machine Learning Lifecycle management?

What is DevOps?

DevOps is considered to be the union of the two most important functions required for successful product delivery that is the development and the operations. The main purpose of adopting DevOps is to assure continuous delivery of value to the end-users through the productive union of people, products and processes.

DevOps combines the software development process along with information technology operations in order to optimize the time and resources involved in the complete system development life cycle. You should adopt DevOps in order to closely align your daily tasks such as delivering features, fixes, and updates timely with your project goals and objectives.

Some of the well-defined goal and benefits of DevOps spanning the entire delivery pipeline are provided below.

  • Improvement in deployment frequency
  • Quicker time to market
  • The substantial decrease in the failure rate of new releases
  • Shortening of lead times between the fixes
  • Lesser mean time to recovery

Relationship of DevOps and Machine Learning

The combination of DevOps and Machine Learning is resulting in great improvisations in lifecycle management. The utilization of machine learning in DevOps has already brought substantial ease in management, monitoring, and version modelling. The adoption of machine learning techniques in mobile app development services can simplify the workflows and the overall collaboration process. 

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Nowadays it has been observed that effective management of machine learning lifecycle has become very critical for successful completion of your mobile app development services that is DevOps.Many leading solution providers describe essential functions that are accelerated with the incorporation of DevOps services with machine learning lifecycle management.

  • Faster preparation of data
  • Improvisation in experimentation procedures
  • Effective model training and management
  • Quicker deployment and monitoring
  • Enhancement in the governance process and readability
  • Better collaboration throughout the model development process
  • Easy management of large data and daily computational operations

One of the most important functions is easy management of complex data pipelines along with the creation of appropriate data models that can easily feed your data in the mobile app development services.

Let us look into the machine learning pipelines along with their important roles and functions.

What role does machine learning pipeline play?

Machine learning pipeline is known for allowing modularization of different phases into discrete steps. The pipelines play a very important role in providing a mechanism for automation, sharing and reproducing models along with machine learning assets.

All the machine learning pipelines create and manage workflows that successfully stitch together machine learning phases. Most importantly the pipelines allow you to easily optimize your workflow with speed, simplicity, reusability, and portability. The research defines the basic four steps that are very much involved in deploying machine learning pipelines.

The following steps form a complete machine learning pipeline and are used by various data scientists, engineers, IT experts to collaborate.

  • Simple Data Preparation and Ingestion
  • Continuous Model Training and Retraining
  • Comprehensive Model Evaluation
  • Successful and Quick Deployment

Usage of distinct steps creates the possibility to only rerun the steps required along with your tweaking and testing of your workflow. A step is usually defined as a computational unit in the DevOps services pipeline. Generally, the task of preparation of data involves multiple steps.

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The most importantly defined features of a machine learning pipeline consist of transformation, validation, featurization, and normalization. One of the notable features of a machine learning pipeline, when used across DevOps, is the reuse of data sources and intermediate data resulting in the savior of valuable computational time and resources.

What are the advantages of boosting machine learning pipelines for DevOps?

Machine Learning pipelines have all the potential to become the required primary tool for computing, assessing and decision-making procedures in DevOps. Through boosting of machine learning pipeline DevOps teams can evolve the complete procedure of development, deliverance, deployment, and organization for the mobile app development services.

Accelerating of machine learning pipelines can improve the performance and simplify all the business operations for the DevOps. Experts define various advantages of accelerating the machine learning pipelines, especially for the DevOps.

Easy Collaboration across teams

Substantial increase in visibility and accessibility for all team members results in easy collaboration across the team. Microsoft Azure allows you to create machine learning pipelines visible to all the team members working on the project. Python can be used to create machine learning pipelines very much transparent and interactive through Jupiter notebooks.

Simplification of Workflows

Now you can easily templatize pipelines for general scenario and deploy them accurately to a REST endpoint.  This procedure can help you to schedule batch scoring or retraining jobs. Boosting of machine learning pipelines results in imperative constructs for sequencing along with parallelizing the steps present in your pipelines when there is no case of data dependency. 

You are now only required to rerun the steps you need along with the tweaking and testing your workflow while rerunning a pipeline. The workflow through DevOps is clearly visible using accelerated machine learning pipelines.

Centralization of Management

Boosting the utilization of machine learning pipelines enables DevOps to easily track models, their version histories, their artifacts along with their lineage leading to centralization. Some models like Microsoft Azure allow you to easily submit and track individual pipelines runs. 

This means you can easily manage all the data and scripts in a separate manner resulting in increased productivity. Thus all the procedures such as computation of targets, tracking of metrics along with monitoring of all the data are simplified leading to very centralized management.

Simple tracking of your experiments

The improvement of productivity by allowing management of models deployed in the clouds and experiment tracking showcases the benefits for DevOps capabilities for machine learning.

Creation of multiple workspaces or common workplaces sharable by multiple people leads to easy tracking of all the experiments, training runs, inclusive of logs, metrics, output, and snapshots of the scripts.

How does a machine learning pipeline influence DevOps?

Market research reveals some of the major positive influences that machine learning has impacted upon DevOps.

Enhancement in data accessibility

Machine learning pipelines along with the use of artificial intelligence allows the collection of data from multiple sources. 

The pipelines prepare the data for robust and reliable evaluation thus enhancing the overall data accessibility.

Increased efficacy for implementation

Use of artificial intelligence has resulted in self-governance allowing teams to shift towards automation and improvisation. Thus overall management system has become more of self-dependent.

Effective use of resources

The task automation and routine management led by machine learning pipeline algorithms optimize the use of valuable time and resources along with the reduction of human complexity.


Thus streamlining of DevOps using machine learning pipelines and algorithms adds more visibility into experiment metrics, training and model versions. Machine learning services seamlessly works with DevOps services providing end to end capabilities for the fully functional lifecycles. The use of machine learning pipeline models adds the much-required speed and simplicity into the DevOps.

Studies reveal that machine learning pipelines models help DevOps teams to focus on innovation and creativity by the elimination of all the inefficiencies across the operational life cycles. Advance technology empowers the DevOps teams to beautifully manage the amount, variability and speed of data.

Use of higher technologies and pipeline architecture has already resulted in enhancement of automation along with the overall rise in efficiency of DevOps team.

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