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Machine Learning Engineer
This three-day, advanced-level course helps experienced data scientists build, train, and deploy ML models for any use case using fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. The course includes presentations, demonstrations, discussions, and labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.
Day 1
1. Setting Up Amazon SageMaker Studio
- JupyterLab Extensions in SageMaker Studio
- Demonstration: SageMaker User Interface Demo
2. Data Processing
- Using SageMaker Data Wrangler for data processing
- Hands-On Lab: Analyze and Prepare Data Using Amazon SageMaker Data Wrangler
- Using Amazon EMR
- Hands-On Lab: Analyze and Prepare Data at Scale Using Amazon EMR
- Using AWS Glue interactive sessions
- Using SageMaker Processing with Custom Scripts
- Hands-On Lab: Data Processing Using Amazon SageMaker Processing and the SageMaker Python SDK
- SageMaker Feature Store
- Hands-On Lab: Feature Engineering Using SageMaker Feature Store
3. Model Development
- SageMaker training jobs
- Built-in algorithms
- Bring Your Own Script
- Bring Your Own Container
- SageMaker Experiments
- Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
Day 2
3: Model Development (continued)
- SageMaker Debugger
- Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
- Automatic model tuning
- SageMaker Autopilot: Automated ML
- Demo: SageMaker Autopilot
- Bias detection
- Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
- SageMaker Jumpstart
4. Deployment and Inference
- SageMaker Model Registry
- SageMaker Pipelines
- Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- SageMaker model inference options
- Scaling
- Testing Strategies, Performance, and Optimization
- Hands-On Lab: Inferencing with SageMaker Studio
5. Monitoring
- Amazon SageMaker Model Monitor
- Discussion: Case Study
- Demonstration: Model Monitoring
Day 3
6: Managing SageMaker Studio Resources and Updates
- Accrued costs and shutdown
- Updates
Capstone
- Environment Setup
- Challenge 1: Analyze and prepare the dataset using SageMaker Data Wrangler
- Challenge 2: Create feature groups in the SageMaker Feature Store
- Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
- (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
- Challenge 5: Evaluate the model for bias using SageMaker Clarify
- Challenge 6: Perform batch predictions using the model endpoint
- (Optional) Challenge 7: Automate the entire model development process using SageMaker Pipeline
- Accelerating the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
- Using the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
This course includes presentations, demonstrations, practice labs, discussions, and a capstone project.
- Experienced data scientists who are proficient in the fundamentals of machine learning and deep learning.
- Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
Furthermore, this course is intended for the following job roles:
- Machine Learning & AI
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