Production Machine Learning Systems

By Google Cloud
In this course, we will dive into the components and best practices of a high-performing ML system in production environments.
Course info
Level
Advanced
Updated
Sep 17, 2019
Duration
3h 17m
Table of contents
Welcome to the course
Course Introduction 3m Getting started with GCP and Qwiklabs 4m
Architecting Production ML Systems
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
Introduction 5m Data On-Premise 3m Large Datasets 4m Data on Other Clouds 2m Existing Databases 2m Demo:Load data into BigQuery 5m Demo:Automatic ETL Pipelines into GCP 4m
Designing Adaptable ML systems
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
Introduction 1m Training 6m Predictions 3m Why distributed training? 5m Distributed training architectures 6m Faster input pipelines 3m Native TensorFlow Operations 3m TensorFlow Records 1m Parallel pipelines 6m Data parallelism with All Reduce 5m Parameter Server Approach 3m Inference 4m
Hybrid ML systems
Introduction 5m Machine Learning on Hybrid Cloud 5m KubeFlow 3m Demo:KubeFlow 22m Lab: Kubeflow: End to End 0m Embedded Models 3m TensorFlow Lite 2m Optimizing for Mobile 6m Summary 2m
Course Summary
Summary 2m
Description
Course info
Level
Advanced
Updated
Sep 17, 2019
Duration
3h 17m
Description

In this course, we will dive into the components and best practices of a high-performing ML system in production environments.

About the author
Google Cloud
About the author

Build, innovate, and scale with Google Cloud Platform.

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