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.
Learn MLOps for Machine Learning
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Intermediate level
Recommended experience
6 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Capitalize on MLOps as an emerging field. Data-focused companies are looking for engineers with these skill sets.
Build a basic MLOps pipeline from scratch with open-source tools - take a working template with you for your own projects.
Take ChatGPT into account to provide a practical bridge for engineers and DevOps teams.
Skills you'll gain
Details to know
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Recently updated!
August 2025
Assessments
5 assignments
Taught in English
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There is 1 module in this course
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.
What's included
35 videos5 assignments
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
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