Production Machine Learning Systems
In this course, we will dive into the components and best practices of a high-performing ML system in production environments.
What you'll learn
In this course, we will dive into the components and best practices of a high-performing ML system in production environments.
Table of contents
Welcome to the course
7mins
Architecting Production ML Systems
38mins
- Introduction 2m
- The Components of an ML System 2m
- The Components of an ML System:Data Analysis and Validation 4m
- The Components of an ML System:Data Transformation + Trainer 2m
- The Components of an ML System:Tuner + Model Evaluation and Validation 2m
- The Components of an ML System:Serving 1m
- The Components of an ML System:Orchestration + Workflow 4m
- The Components of an ML System:Integrated Frontend + Storage 2m
- Training Design Decisions 5m
- Serving Design Decisions 5m
- Lab Intro:Serving on Cloud AI Platform 2m
- Serving on Cloud AI Platform 0m
- Lab Solution:Serving on Cloud AI Platform 4m
- Designing from Scratch 3m
Ingesting data for Cloud-based analytics and ML
25mins
Designing Adaptable ML systems
34mins
- Introduction 4m
- Adapting to Data 3m
- Changing Distributions 3m
- Exercise:Adapting to Data 2m
- Right and Wrong Decisions 3m
- System Failure 2m
- Mitigating Training-Serving Skew through Design 1m
- Lab Intro:Serving ML Predictions in batch and real-time 2m
- Lab: Serving ML Predictions in batch and real-time 0m
- Lab Solution:Serving ML Predictions in batch and real-time 9m
- Debugging a Production Model 4m
- Summary 1m
Designing High-performance ML systems
46mins
Hybrid ML systems
48mins
Course Summary
2mins
