Introduction to MLOps

Hello There!

In this article, we will learn what is MLOps or Machine Learning Operations. I will try to simplify the vast and intriguing world of ML Operations and its associated infrastructure.

What is MLOps ?

MLOps is a set of operation practices that we follow on machine learning applications. Just like a tranditional Sofware development we will be able to simplify the management process of it, by automating few of the steps.

How that be useful ?

When we completed building a machine learning application, we tend to deploy it to a api, and that it. Let’s for example, Once after your deployment within few months, upon evalution, your model performace is getting down.. do you know why ?

There can be multiple reson for this, like may be data that we trained with and the data we are predicting on is getting different. so.. to track all of these, we need to build a end to end system flow, where we can track and monitor imporovise things.

In this way we can get less operations issue, just can focus on imporiving our existing set of models.

Applying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning and Deep Learning models in large-scale production environments.

DevOps vs MLOps

You must have heard of good old DevOps. The process to build and deploy software applications. You might wonder how MLOps is different.

The DevOps stages are targeted for developing a software application. You plan the features of the application you want to release, write code, build the code, test it, create a release plan and deploy it. You monitor the infrastructure where the app is deployed. And this cycle continues until the app is fully built.

In MLOps, things are different. We implement the following stages

ML Workflow Lifecycle

Every ML project aims to build a statistical model out of the data, applying a machine learning algorithm. Hence, Data and ML Model come out as two different artifacts to the software development of the Code Engineering part. In general, ML Lifecycle consists of three elements:

• Data Engineering: supplying and learning datasets for ML algorithms. It includes data ingestion, exploration and validation, cleaning, labeling, and splitting (into the training, validation, and test dataset).
• Model Engineering: preparing a final model. It includes model training, evaluation, testing, and packaging.
• Model Deployment: integrating the trained model into the business application. Includes model serving, performance monitoring, and performance logging.

MLOps Pros and MLOps Costs

Its all about Introduction of MlOps Happy Hacking Guys….