Module 1: Introduction
- Overview of key Machine Learning and Deep Learning concepts
- Getting about in AWS
- Overview of SageMaker features
- Taking your first look at SageMaker studio
Module 2: Preparing your dataset
- Identifying your data and articulating your problem
- Format data for consistency
- Cleaning and validating your data
- Uploading to SageMaker
Module 3: Data Analysis
- Clustering
- Trend analysis
- Finding other relationships between different types of data
Module 4: Data Visualisation
- Frequency tables
- Cross-tabulation tables
- Bar charts
- Line graphs
- Pie charts
- Heat Maps
- Scatter graphs
Module 5: Training your Model
- Creating a Training job
- Assigning Compute resources
- Selecting the right algorithm
- Overview of using custom code (Python, TensorFlow)
Module 6: Deploying your Model
- SageMaker Hosting Services
- Configuring and creating an HTTPS endpoint
Module 7: SageMaker Batch Transforms
- Making inferences from your dataset
- Indexing and real-time indices
- Using Batch Transform to preprocess data to train a new model
Module 8: Validating your Model
- SageMaker Debugger
- Offline testing
- Online testing
- Validating using a holdout set
Module 9: Model Tuning
- Defining metrics
- Hyperparameter tuning
- Automatic model tuning
Module 10: Deploying and sharing using SageMaker Feature Store
- Creating, Storing and Sharing Features
- Online / Offline
- Feature Groups
- Discovery
- Batch Inference
- Feature Data Ingestion