Learn MLOps for Machine Learning

Overview

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When teams are working with machine learning models, changing features, different data sets, new algorithms, and unique computing resources all influence a machine learning model's performance. Tracking all of these items can be complicated. With tools such as DVC, MLFlow, AWS, you can meet the challenge. Milecia McGregor demonstrates how to use MLOps tools to improve machine learning and automate some of the steps in the process.

Syllabus

Learn MLOps for Machine Learning

This module introduces MLOps for machine learning engineers, covering the end-to-end pipeline from data collection to production deployment. Learners explore data handling and versioning, model creation and experiment tracking, and best practices for deploying and monitoring models in production. Through practical workflows and industry-standard tools, the course emphasizes automation, reproducibility, and maintaining robust ML systems, equipping participants to apply MLOps principles to real-world projects.