Machine Learning Modeling Pipelines in Production (Coursera)

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

What You Will Learn

- Apply techniques to manage modeling resources and best serve batch and real-time inference requests.

- Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.

Course 3 of 4 in the Machine Learning Engineering for Production (MLOps) Specialization

Syllabus

WEEK 1

Neural Architecture Search

Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.

WEEK 2

Model Resource Management Techniques

Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.

WEEK 3

High-Performance Modeling

Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.

WEEK 4

Model Analysis

Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.

WEEK 5

Interpretability

Learn about model interpretability - the key to explaining your model’s inner workings to laypeople and expert audiences and how it promotes fairness and helps address regulatory and legal requirements for different use cases.