Overview
- Course level: Advanced
- Duration: 3 days
- Activities
Prerequisites
- Experience using ML frameworks
- Python programming experience
- At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
- AWS Technical Essentials
Delegates will learn how to
Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio
Outline
- JupyterLab Extensions in SageMaker Studio
- Demonstration: SageMaker user interface demo
- Using SageMaker Data Wrangler for data processing
- Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
- Using Amazon EMR
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
- 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
- SageMaker Jumpstart
- SageMaker Model Registry
- SageMaker Pipelines
- SageMaker model inference options
- Scaling
- Testing strategies, performance, and optimization
- Amazon SageMaker Model Monitor
- Discussion: Case study
- Demonstration: Model Monitoring
- Accrued cost and shutting down
- Updates
- Capstone
- 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
- 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
- Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
- Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
- Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
- Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- Hands-On Lab: Inferencing with SageMaker Studio
Frequently asked questions
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Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.
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QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
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When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.