ML Ops

Data Preparation for Machine Learning
Feature Engineering
Data Versioning for ML
Serving ML Models in Production

What is MLOps?

MLOps = DevOps + DataOps + ModelOps¹

Machine Learning Operations (MLOps) is a set of practices that includes Machine Learning, DevOps and Data Engineering elements. The main aim of the union is reliable and efficient deployment and maintenance of Machine Learning systems in production.

Features of MLOps:

Ensures validation of data, data schemas, and models along with testing and validation of code
Facilitates automated deployment of a ML pipeline that should automatically deploy a model and corresponding prediction service.
Expedites the process of automatically re-training and serving the models.

Components

Workflow

The following table summarizes MLOps main practices and how they relate to DevOps and Data Engineering practices:

What is MLOps Engineer?

Quote

ML Engineers build and retrain machine learning models. MLOps Engineers enable the ML Engineers. MLOps Engineers build and maintain a platform to enable the development and deployment of machine learning models. They typically do that through standardization, automation, and monitoring. MLOps Engineers reiterate the platform and processes to make the machine learning model development and deployment quicker, more reliable, reproducible, and efficient.

However, ML Engineers focus on building, training and validating machine learning models, while MLOps Engineers concentrate primarily on testing, deploying and monitoring models in production environments.

Noted

Practical MLOps — MLflow