MLOps (Machine Learning Operations) Fundamentals

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Google Cloud

5:31:08

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1. Welcome to MLOps Fundamentals

1. Course Introduction.mp4

01:26

2. Why and When do we Need MLOps

1. Data Scientists Pain Points.mp4

02:23

2. The concept of DevOps in ML.mp4

06:33

3. Machine Learning Lifecycle.mp4

06:14

03. Understanding the Main Kubernetes Components (Optional)

01. Introduction.mp4

01:12

02. Introduction to Containers.mp4

05:59

03. Containers and Container Images.mp4

06:44

04. Lab Intro.mp4

00:33

05. Pluralsight - Getting Started with GCP and Qwiklabs.mp4

03:48

07. Lab solution.mp4

05:20

08. Introduction to Kubernetes.mp4

03:39

09. Introduction to Google Kubernetes Engine.mp4

03:07

10. Compute Options Detail.mp4

10:18

11. Kubernetes Concepts.mp4

04:28

12. The Kubernetes Control Plane.mp4

06:20

13. Google Kubernetes Engine Concepts.mp4

06:19

14. Lab Intro.mp4

00:30

16. Lab solution.mp4

04:37

17. Deployments.mp4

04:08

18. Ways to Create Deployments.mp4

02:47

19. Services and Scaling.mp4

01:50

20. Updating Deployments.mp4

01:54

21. Rolling Updates.mp4

03:12

22. Blue-Green Deployments.mp4

05:20

23. Canary Deployments.mp4

08:19

24. Managing Deployments.mp4

01:03

25. Lab Intro.mp4

00:39

27. Jobs and CronJobs.mp4

04:51

28. Parallel Jobs.mp4

03:39

29. CronJobs.mp4

03:49

4. Introduction to AI Platform Pipelines

1. Overview.mp4

08:49

2. Introduction to AI Platform Pipelines.mp4

08:56

3. Concepts.mp4

06:30

4. When to use.mp4

07:34

5. Ecosystem.mp4

05:40

7. Lab Solution.mp4

03:56

5. Training, Tuning and Serving on AI Platform

1. System and concepts overview.mp4

06:35

2. Create a reproducible dataset.mp4

08:22

3. Implement a tunable model.mp4

10:03

4. Build and push a training container.mp4

03:40

5. Train and tune a model.mp4

05:15

6. Serve and query a model.mp4

02:15

7. Lab Intro.mp4

00:43

9. Lab Solution.mp4

45:00

6. Kubeflow Pipelines on AI Platform

1. System and concept overview.mp4

08:25

2. Describing a Kubeflow Pipeline with KF DSL.mp4

05:34

3. Pre-built components.mp4

05:01

4. Lightweight Python Components.mp4

02:55

5. Custom components.mp4

04:58

6. Compile, Upload and Run.mp4

04:45

7. Lab Intro - Continuous Training Pipeline with Kubeflow Pipeline and Cloud AI Platform.mp4

00:38

9. Lab Solution.mp4

39:09

7. CICD for Kubeflow Pipelines on AI Platform

1. Concept Overview.mp4

02:21

2. Cloud Build Builders.mp4

02:43

3. Cloud Build Configuration.mp4

04:34

4. Cloud Build Triggers.mp4

03:32

5. Lab Intro.mp4

00:15

8. Summary

1. Summary.mp4

01:59

Description

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

What You'll Learn?

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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Machine Learning

Google Cloud

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Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

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.