Production Machine Learning Systems

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Google Cloud

3:17:47

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00. Welcome to the course

00. Course Introduction.mp4

02:51

01. Getting started with GCP and Qwiklabs.mp4

03:48

01. Architecting Production ML Systems

00. Introduction.mp4

02:12

01. The Components of an ML System.mp4

01:38

02. The Components of an ML System - Data Analysis and Validation.mp4

04:21

03. The Components of an ML System - Data Transformation + Trainer.mp4

01:39

04. The Components of an ML System - Tuner + Model Evaluation and Validation.mp4

02:05

05. The Components of an ML System - Serving.mp4

01:18

06. The Components of an ML System - Orchestration + Workflow.mp4

03:44

07. The Components of an ML System - Integrated Frontend + Storage.mp4

01:31

08. Training Design Decisions.mp4

04:33

09. Serving Design Decisions.mp4

05:11

10. Lab Intro - Serving on Cloud AI Platform.mp4

01:34

11. Serving on Cloud AI Platform.mp4

00:10

12. Lab Solution - Serving on Cloud AI Platform.mp4

03:31

13. Designing from Scratch.mp4

02:38

02. Ingesting data for Cloud-based analytics and ML

00. Introduction.mp4

05:14

01. Data On-Premise.mp4

03:02

02. Large Datasets.mp4

03:41

03. Data on Other Clouds.mp4

01:32

04. Existing Databases.mp4

02:29

05. Demo - Load data into BigQuery.mp4

05:18

06. Demo - Automatic ETL Pipelines into GCP.mp4

04:06

03. Designing Adaptable ML systems

00. Introduction.mp4

03:56

01. Adapting to Data.mp4

02:52

02. Changing Distributions.mp4

03:25

03. Exercise - Adapting to Data.mp4

01:52

04. Right and Wrong Decisions.mp4

03:09

05. System Failure.mp4

01:56

06. Mitigating Training-Serving Skew through Design.mp4

01:21

07. Lab Intro - Serving ML Predictions in batch and real-time.mp4

01:40

08. Serving ML Predictions in batch and real-time.mp4

00:10

09. Lab Solution - Serving ML Predictions in batch and real-time.mp4

08:51

10. Debugging a Production Model.mp4

03:35

11. Summary.mp4

00:59

04. Designing High-performance ML systems

00. Introduction.mp4

01:21

01. Training.mp4

05:48

02. Predictions.mp4

02:51

03. Why distributed training - .mp4

04:47

04. Distributed training architectures.mp4

06:27

05. Faster input pipelines.mp4

03:12

06. Native TensorFlow Operations.mp4

03:00

07. TensorFlow Records.mp4

01:26

08. Parallel pipelines.mp4

06:15

09. Data parallelism with All Reduce.mp4

04:38

10. Parameter Server Approach.mp4

02:34

11. Inference.mp4

03:59

05. Hybrid ML systems

00. Introduction.mp4

05:04

01. Machine Learning on Hybrid Cloud.mp4

05:15

02. KubeFlow.mp4

02:47

03. Demo - KubeFlow.mp4

21:37

04. Kubeflow - End to End.mp4

00:10

05. Embedded Models.mp4

02:43

06. TensorFlow Lite.mp4

02:14

07. Optimizing for Mobile.mp4

05:38

08. Summary.mp4

02:06

06. Course Summary

00. Summary.mp4

02:03

Description

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.

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Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.