Learning Vertex AI: MLOps with Google Cloud

1h 39mIntermediate2023-05-05

Authors

Vaidheeswaran Archana

Data Scientist, AI Engineer, and Data Product Manager

Soham Chatterjee

Machine Learning Lead

Course details

It seems like everyone is talking about AI these days. But did you know you can train and manage machine learning models using the new MLOps cloud solution from Google? In this course, learn how to manage your entire machine learning model’s lifecycle—from loading data to monitoring deployments—using Vertex AI to build models and maintain your applications.

Find out why Vertex AI has become so popular so quickly, not just within the tech industry, but all across the globe. Instructors Archana Vaidheeswaran and Soham Chatterjee show you the skills you need to know to get up and running with this powerful new tool. Along the way, take your Python and ML skills to the next level with data loading, feature engineering, training models and hyperparameter tuning, model deployment, and model monitoring.

Skills covered

Vertex AIMachine LearningGoogleArtificial Intelligence (AI)Learning

Concepts

0. Introduction

01 - Learning Vertex AI
02 - What you should know

1. Introduction to MLOps

03 - MLOps lifecycle - ML development
04 - MLOps lifecycle - Training
05 - MLOps lifecycle - Deployment
06 - Vertex AI walkthrough

2. Feature Engineering

07 - Vertex AI feature store
08 - Vertex AI data labeling

3. Training and Hyperparameter

09 - Vertex AI AutoML
10 - Vertex AI experiments
11 - Vertex AI model registry
12 - Vertex AI training

4. Model Serving and Deployment

13 - Vertex AI prediction
14 - Vertex AI TensorBoard
15 - Vertex AI endpoints

5. Model Monitoring and Management

16 - Vertex AI model monitoring
17 - Vertex AI pipelines

6. Deploy Your Project Using Vertex AI

18 - Challenge - Deploy a project
19 - Solution - Deploy a project

Conclusion

20 - Vertex AI vs. other MLOps platforms
21 - What next