MLOps - Introduction to DevOps

As the first thought stuck in your mind, MLops is DevOps of machine learning. MLops is basically an extension of DevOps methodology including machine learning. While the goal is to develop ML products for the end customer at scale and in minimum time, it has many challenges to be addressed mainly in automating and operating. MLops addresses this issue. MLops is an inclusion of several parts like best practices, set of concepts, and development culture.

Machine Learning has opened doors for businesses to innovate more by leveraging data letting companies be more efficient, and accurate. While there are many ML projects in research, very few of them reach the production stage. This resembles that the ML community focuses solely on building models rather than building production-grade ML products. Many ML workflows are being manually managed in industrial applications.

In this, we will study how manual ML workflow can be automated resulting in ML research getting into production. There are various meanings to the word MLOps in the world that have the least error, First, let’s brush up on the basics of DevOps.

Foundations of DevOps

Various methodologies like agile and water methodologies have been followed. An alternative "DevOps" has been introduced to counter the issues caused by traditional methods while having the same goal of delivering production-ready software. It’s basically a way of solving the technical issues in organizations.DevOps bridges the gap between development, operations, and collaboration. Continuous Integration(CI) and Continuous deployment (CD) are being automated resulting in reliable software products. Continuous testing(CT) quality assurance ensures the production quality of code with frequent monitoring and feedback.

There are many tools for DevOps available in the market, they can be broadly categorized into 6 groups, They are

1) Collaboration and knowledge sharing - Slack, Trello, Gitlab wiki.

2) Source Code management - Github, Gitlab

3) Build process - Maven]

4) Continuous Integration - Jenkins, Gitlab CI

5) Deployment automation - Kubernetes, Docker

6) Monitoring and logging - Prometheus, LOgstash

Ready-to-use DevOps tools available with Cloud are basically designed for cloud use resulting in increased efficiency. DevOps ensures the quality of production software, making it gain a massive following among software organizations. The same experience with DevOps is now being used in ML to automate the development, operation, and collaboration of Machine Learning software.

We will be discussing more about Mlops in the upcoming posts. So stay tuned.

Thank you.