MLOps

MLOps is a new term in Data Science that is giving rise to a new set of technologies and skill sets. MLOps is an acronym for Machine Learning Operations and derives itself from DevOps which is used in Development operations.

MLOps is an ensemble of processes or practices for Enterprises to run AI operations successfully. Most Enterprises develop AI/ML models over a period of time and deploy it on a regular basis into production. MLOps covers the the complete life cycle of Model development, integration and testing to ensure the quality of the model output. It is a bridge between the Data Scientists who develop and optimize the AI/ML models to the Operations management that run and maintain the model.

There are three main steps in MLOps

• Continuous Integration covering the testing and validation of code, data schema, data and the AI/ML models.
• Continuous Delivery covering the deployment of new models, training pipelines and managing the output
• Continuous Training covering the automatic retraining and revalidation of the models to maintain/improve the accuracy and relevance

MLOps is tough because once the Data Scientists develop and test the model with data, the same needs to be deployed at production scale with the required data pipelines and continuously monitored for quality. 

MLOps is fast emerging as one of the hottest fields, with a great demand for MLOps engineers.

ML Ops engineers need

• Platform skills like Azure, AWS, GCP etc.
• Ability to analyze and build Data pipelines
• Understand the AI/ML model testing processes
• Knowledge of relevant MLOps tools