This comprehensive course is designed for aspiring MLOps engineers and data scientists looking to bridge the gap between experimental notebooks and robust production environments. You will begin by establishing a strong foundation in model development, exploring the hardware essentials of CPUs and GPUs, and mastering hyperparameter tuning. The curriculum moves rapidly into industrial-grade experimentation using MLflow, where you will learn to track parameters, manage model artifacts, and control versioning through hands-on labs.
Deploy ML Models to Production
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Beginner level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
Details to know
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Recently updated!
March 2026
Assessments
3 assignments
Taught in English
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There are 3 modules in this course
This module focuses on the transition of machine learning models from static files to live, scalable services. You will explore the differences between online and offline serving architectures and learn to handle model drift to ensure long-term accuracy. By the end of this module, you will be proficient in using BentoML to package, deploy, and upgrade model versions in a production environment.
What's included
6 videos1 reading1 assignment
This module covers the legal and ethical framework of MLOps, focusing on data privacy, security, and global compliance standards like GDPR and HIPAA. You will learn to manage data access and retention policies to protect sensitive information.
What's included
9 videos3 readings1 assignment
This module provides a deep dive into the AWS SageMaker ecosystem, preparing you to manage the full ML lifecycle on a leading cloud platform.
What's included
3 videos1 assignment
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