Explore the concept of MLOps and learn to build an automated workflow covering the entire lifecycle of a machine learning model in this 30-minute webinar from Open Data Science. Discover how to extend the power of Git and GitHub using open-source tools to create an efficient MLOps workflow. Cover topics including the importance of MLOps, common pain points, problem-solving approaches, DVC Pipeline implementation, shared reproducibility, CML Runner utilization, and leveraging GitHub Actions for automation. Gain practical insights into streamlining the process from data labeling to monitoring predictions, making MLOps more accessible and efficient.
Overview
Syllabus
Intro
Why MLOps
Pain Points
The Problem
The Idea
DVC Pipeline
Shared Reproducibility
CML Runner
Github
GitHub Actions
Taught by
Open Data Science
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