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
Related Topics for further study
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
IntermediateCourse 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
- Google Cloud Certified - Professional Data Engineer
- Google Cloud Certified - Professional Machine Learning Engineer
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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