📁 Folder Tree
- 📁 Building Machine Learning Pipelines on AWS
- 📂 CHAPTER 01 Introduction
- 📂 01-01 Course Introduction
- 📂 01-02 Dataset Overview
- 📂 CHAPTER 02 Training Models with SageMaker
- 📂 02-01 Introduction to Training Models with SageMaker
- 📂 02-02 Machine Learning with Amazon SageMaker
- 📂 02-03 Built-In Algorithms
- 📂 02-04 Demo Training a Model with SageMaker
- 📂 02-05 Model Deployment with SageMaker
- 📂 02-06 Demo Deploying a Model
- 📂 02-07 Review of Training Models with SageMaker
- 📂 CHAPTER 03 SageMaker Pipelines
- 📂 03-01 Introduction to SageMaker Pipelines
- 📂 03-02 SageMaker Pipelines
- 📂 03-03 Demo Creating a Pipeline
- 📂 03-04 Pipeline Parameters
- 📂 03-05 Demo Adding Parameters to a Pipeline
- 📂 03-06 Quality Gates
- 📂 03-07 Demo Adding a Quality Gate to a Pipeline
- 📂 03-08 Deploying a Model
- 📂 03-09 Demo Automatic Model Deployment
- 📂 03-10 Review of Creating SageMaker Pipelines
- 📂 CHAPTER 04 Extending SageMaker Pipelines
- 📂 04-01 Introduction to Extending SageMaker Pipelines
- 📂 04-02 Data Processing with Pipelines
- 📂 04-03 Demo Implementing a Processing Step
- 📂 04-04 Custom Algorithms in SageMaker
- 📂 04-05 Demo Using a Custom Algorithm in SageMaker Pipelines
- 📂 04-06 Hyperparameter Optimization in Pipelines
- 📂 04-07 Demo Selecting a Model with the Highest Accuracy
- 📂 04-08 Review of Extending SageMaker Pipelines
- 📂 CHAPTER 05 Using Other Services with SageMaker Pipelines
- 📂 05-01 Introduction to Using Other Services
- 📂 05-02 Implementing MLOps with Projects
- 📂 05-03 Demo Creating a SageMaker Project
- 📂 05-04 Using AutoML
- 📂 05-05 Improving Data and Model Quality
- 📂 05-06 Summary of Using Other Services with SageMaker Pipelines
- 📂 CHAPTER 06 Conclusion
- 📂 06-01 Course Summary
- 📂 06-02 Conclusion and What's Next