Live-Online: Amazon SageMaker Studio for Data Scientists

The three-day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, youll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

Day 11. Amazon SageMaker Studio Setup

• 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 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 23 Model Development (continued)

• SageMaker Debugger
• Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
• Automatic model tuning
• SageMaker Autopilot: Automated ML
• Demonstration: 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 36 Managing SageMaker Studio Resources and Updates

• Accrued cost and shutting down
• Updates

Capstone

• Environment setup
• Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
• Challenge 2: Create feature groups in 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 model endpoint
• (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline

Lerndauer: 3 days

Mit dieser Veranstaltung sind sie flexibel: Diese Veranstaltung wird vollständig online ausgeliefert!

Wichtige Information: Bitte beachten Sie, dass einige unserer Webinare auch aus mehreren Online-Modulen bestehen können. Erkennbar ist dies, wenn die Dauer länger als ein Tag ist.

Eine Übersicht der einzelnen Termine zu den Online-Modulen erhalten Sie nach der Buchung in Ihrer persönlichen Online-Lernumgebung.

Objectives

• 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

Traget groups

• Experienced data scientists who are proficient in ML and deep learning fundamentals.
• 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