GK910029 Machine Learning Engineering on AWS

COURSE OBJECTIVE:
In this course, you will learn to do the following: • Explain ML fundamentals and its applications in the AWS Cloud. • Process, transform, and engineer data for ML tasks by using AWS services. • Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability. • Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration. • Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows. • Discuss appropriate security measures for ML resources on AWS. • Implement monitoring strategies for deployed ML models, including techniques for detecting data drift.

TARGET AUDIENCE:
This course is designed for professionals who are interested in building, deploying, and operationalizing machine learning models on AWS. This could include current and in-training machine learning engineers who might have little prior experience with AWS. Other roles that can benefit from this training are DevOps engineer, developer, and SysOps engineer.

COURSE PREREQUISITES:
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)

COURSE CONTENT:
Day 1 Module 0: Course IntroductionModule 1: Introduction to Machine Learning (ML) on AWSTopic 1A: Introduction to ML
Topic 1B: Amazon SageMaker AI
Topic 1C: Responsible MLModule 2: Analyzing Machine Learning (ML) ChallengesTopic 2A: Evaluating ML business challenges
Topic 2B: ML training approaches
Topic 2C: ML training algorithmsModule 3: Data Processing for Machine Learning (ML)Topic 3A: Data preparation and types
Topic 3B: Exploratory data analysis
Topic 3C: AWS storage options and choosing storageModule 4: Data Transformation and Feature EngineeringTopic 4A: Handling incorrect, duplicated, and missing data
Topic 4B: Feature engineering concepts
Topic 4C: Feature selection techniques
Topic 4D: 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 SDKDay 2 Module 5: Choosing a Modeling ApproachTopic 5A: Amazon SageMaker AI built-in algorithms
Topic 5B: Selecting built-in training algorithms
Topic 5C: Amazon SageMaker Autopilot
Topic 5D: Model selection considerations
Topic 5E: ML cost considerationsModule 6: Training Machine Learning (ML) ModelsTopic 6A: Model training concepts
Topic 6B: Training models in Amazon SageMaker AI
Lab 3: Training a model with Amazon SageMaker AIModule 7: Evaluating and Tuning Machine Learning (ML) modelsTopic 7A: Evaluating model performance
Topic 7B: Techniques to reduce training time
Topic 7C: Hyperparameter tuning techniques
Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AIModule 8: Model Deployment Strategies Topic 8A: Deployment considerations and target options
Topic 8B: Deployment strategies
Topic 8C: Choosing a model inference strategy
Topic 8D: Container and instance types for inference
Lab 5: Shifting Traffic A/BDay 3 Module 9: Securing AWS Machine Learning (ML) ResourcesTopic 9A: Access control
Topic 9B: Network access controls for ML resources
Topic 9C: Security considerations for CI/CD pipelinesModule 10: Machine Learning Operations (MLOps) and Automated DeploymentTopic 10A: Introduction to MLOps
Topic 10B: Automating testing in CI/CD pipelines
Topic 10C: Continuous delivery services
Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker StudioModule 11: Monitoring Model Performance and Data QualityTopic 11A: Detecting drift in ML models
Topic 11B: SageMaker Model Monitor
Topic 11C: Monitoring for data quality and model quality
Topic 11D: Automated remediation and troubleshooting
Lab 7: Monitoring a Model for Data DriftModule 12: Course Wrap-up

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