Machine Learning Engineering on AWS Eğitimi

Who Should Attend?

We recommend that attendees of this course have the following:

• Familiarity with basic machine learning concepts
• Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn
• Basic understanding of cloud computing concepts and familiarity with AWS
• Experience with version control systems such as Git (beneficial but not required)

• Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities
• Gain experience using Amazon SageMaker AI and analytics tools such as Amazon EMR

Day 1

• Module 0: Course Introduction
• Module 1: Introduction to Machine Learning (ML) on AWS
  
  • Topic A: Introduction to ML
  • Topic B: Amazon SageMaker AI
  • Topic C: Responsible ML
• Module 2: Analyzing Machine Learning (ML) Challenges
  
  • Topic A: Evaluating ML business challenges
  • Topic B: ML training approaches
  • Topic C: ML training algorithms
• Module 3: Data Processing for Machine Learning (ML)
  
  • Topic A: Data preparation and types
  • Topic B: Exploratory data analysis
  • Topic C: AWS storage options and choosing storage
• Module 4: Data Transformation and Feature Engineering
  
  • Topic A: Handling incorrect, duplicated, and missing data
  • Topic B: Feature engineering concepts
  • Topic C: Feature selection techniques
  • Topic D: AWS data transformation services
  • Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
  • Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Day 2

• Module 5: Choosing a Modeling Approach
  
  • Topic A: Amazon SageMaker AI built-in algorithms
  • Topic B: Selecting built-in training algorithms
  • Topic C: Amazon SageMaker Autopilot
  • Topic D: Model selection considerations
  • Topic E: ML cost considerations
• Module 6: Training Machine Learning (ML) Models
  
  • Topic A: Model training concepts
  • Topic B: Training models in Amazon SageMaker AI
  • Lab 3: Training a model with Amazon SageMaker AI
• Module 7: Evaluating and Tuning Machine Learning (ML) models
  
  • Topic A: Evaluating model performance
  • Topic B: Techniques to reduce training time
  • Topic C: Hyperparameter tuning techniques
  • Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
• Module 8: Model Deployment Strategies
  
  • Topic A: Deployment considerations and target options
  • Topic B: Deployment strategies
  • Topic C: Choosing a model inference strategy
  • Topic D: Container and instance types for inference
  • Lab 5: Shifting Traffic A/B

Day 3

• Module 9: Securing AWS Machine Learning (ML) Resources
  
  • Topic A: Access control
  • Topic B: Network access controls for ML resources
  • Topic C: Security considerations for CI/CD pipelines
• Module 10: Machine Learning Operations (MLOps) and Automated Deployment
  
  • Topic A: Introduction to MLOps
  • Topic B: Automating testing in CI/CD pipelines
  • Topic C: Continuous delivery services
  • Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio
• Module 11: Monitoring Model Performance and Data Quality
  
  • Topic A: Detecting drift in ML models
  • Topic B: SageMaker Model Monitor
  • Topic C: Monitoring for data quality and model quality
  • Topic D: Automated remediation and troubleshooting
  • Lab 7: Monitoring a Model for Data Drift
• Module 12: Course Wrap-up