MLOps is a set of practices that streamline and automate the machine learning workflow, from model development to deployment and monitoring. It bridges the gap between machine learning engineers and operations teams, ensuring smooth collaboration. By introducing structure and discipline into the ML lifecycle, MLOps makes the process of building, deploying, and maintaining models more efficient, reliable, and scalable, ultimately improving productivity and consistency in real-world applications.

Issues with Data Science practice without MLOps

• Low coding standards: OOPs concept, Modular coding, logging, Exceptions etc.
• No data management: Data Ingestion/Artifacts etc.
• Versioning: Code, Data, Model
• Data pipeline and experimentation
• No CI/CD concept
• Scalability and monitoring (production): Kubernetes, Prometheus, Grafana
• Cross team friction

Version Tracking

• GitHub is used for version control of code, helping teams track changes, collaborate, and manage different versions of the project.
• DVC (Data Version Control) is used for tracking data and machine learning models, ensuring reproducibility of experiments and smooth collaboration when working with large datasets.

Together, GitHub and DVC bring complete version tracking to an ML project — covering both code and data/models.

Versioning is essential for tracking changes in a project. It allows us to create different versions, manage updates effectively, and retrieve previous states whenever needed.

Below are example steps to follow for enabling version tracking:

If we perform git checkout to any previous version, we can use dvc pull to fetch the data corresponding to that point in time.

Machine Learning Pipeline

ML Pipeline and Basic Components

A Machine Learning pipeline manages all these functions in an end-to-end workflow.

Steps to Create an End-to-End Pipeline(dvc):