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Production Machine Learning Systems

Approx. 8 hours to complete

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Course Summary

This course teaches you how to build, deploy, and scale production machine learning systems using Google Cloud Platform. You will learn how to use various tools and services such as Google Cloud Storage, Google Compute Engine, and TensorFlow to create end-to-end machine learning pipelines.

Key Learning Points

Learn how to build, deploy, and scale production machine learning systems using Google Cloud Platform
Gain hands-on experience using various tools and services such as Google Cloud Storage, Google Compute Engine, and TensorFlow
Create end-to-end machine learning pipelines that can be used in real-world scenarios

Learning Outcomes

Understand how to build, deploy, and scale production machine learning systems using Google Cloud Platform
Gain practical experience using various tools and services such as Google Cloud Storage, Google Compute Engine, and TensorFlow
Create end-to-end machine learning pipelines that can be used in real-world scenarios

Prerequisites or good to have knowledge before taking this course

Basic knowledge of machine learning concepts
Familiarity with Python programming

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Hands-on labs

Similar Courses

Machine Learning on Google Cloud Platform
Building Resilient Streaming Systems on Google Cloud Platform
Data Engineering on Google Cloud Platform

Related Education Paths

Notable People in This Field

Founder of deeplearning.ai
Head of Google AI

Related Books

Description

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

Prerequisites: Basic SQL, familiarity with Python and TensorFlow

Outline

Welcome to the course
Course Introduction
Getting Started with Google Cloud Platform and Qwiklabs
How to Send Feedback

Architecting Production ML Systems
Introduction
The Components of an ML System
The Components of an ML System: Data Analysis and Validation
The Components of an ML System: Data Transformation + Trainer
The Components of an ML System: Tuner + Model Evaluation and Validation
The Components of an ML System: Serving
The Components of an ML System: Orchestration + Workflow
The Components of an ML System: Integrated Frontend + Storage
Training Design Decisions
Serving Design Decisions
Designing from Scratch
Lab Intro: Structured data prediction using AI Platform
Architecting Production ML Systems

Ingesting data for Cloud-based analytics and ML
Introduction
Data On-Premise
Large Datasets
Data on Other Clouds
Existing Databases
Demo: Load data into BigQuery
Demo: Automatic ETL Pipelines into GCP
Ingesting data for Cloud-based analytics and ML

Designing Adaptable ML systems
Introduction
Adapting to Data
Changing Distributions
Exercise: Adapting to Data
Right and Wrong Decisions
System Failure
Mitigating Training-Serving Skew through Design
Lab Intro: Serving ML Predictions in batch and real-time
Lab Solution: Serving ML Predictions in batch and real-time
Debugging a Production Model
Summary
Designing Adaptable ML Systems

Designing High-performance ML systems
Introduction
Training
Predictions
Why distributed training?
Distributed training architectures
Faster input pipelines
Native TensorFlow Operations
TensorFlow Records
Parallel pipelines
Data parallelism with All Reduce
Parameter Server Approach
Inference

Hybrid ML systems
Introduction
Machine Learning on Hybrid Cloud
KubeFlow
Demo: KubeFlow
Embedded Models
TensorFlow Lite
Optimizing for Mobile
Summary

Course Summary
Summary
Additional Resources

Summary of User Reviews

Discover how to build and deploy production-ready machine learning systems on Google Cloud Platform. This course has received positive feedback for its detailed explanations and hands-on approach.

Key Aspect Users Liked About This Course

The hands-on approach and detailed explanations provided in the course have been well received by many users.

Pros from User Reviews

Hands-on approach
Detailed explanations
Real-world examples
Great for beginners
Covers relevant topics

Cons from User Reviews

Some users found the course to be too basic
Not enough focus on advanced topics
Some users found the pace to be slow
Does not cover all GCP services related to ML
Some users found the assignments to be too simplistic

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