Course Overview
Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.
Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.
What you’ll learn
- 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.
Requirements
- 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)
Target audiences
- 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.
Curriculum
- 3 Sections
- 12 Lessons
- 3 Days
Expand all sectionsCollapse all sections
- Day 14
- 1.1Module 1: Introduction to Machine Learning (ML) on AWS Introduction to ML Amazon SageMaker AI Responsible ML
- 1.2Module 2: Analyzing Machine Learning (ML) Challenges Evaluating ML business challenges ML training approaches ML training algorithms
- 1.3Module 3: Data Processing for Machine Learning (ML) Data preparation and types Exploratory data analysis AWS storage options and choosing storage
- 1.4Module 4: Data Transformation and Feature Engineering Handling incorrect, duplicated, and missing data Feature engineering concepts Feature selection techniques 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 24
- 2.1Module 5: Choosing a Modeling Approach Amazon SageMaker AI built-in algorithms Selecting built-in training algorithms Amazon SageMaker Autopilot Model selection considerations ML cost considerations
- 2.2Module 6: Training Machine Learning (ML) Models Model training concepts Training models in Amazon SageMaker AI Lab 3: Training a model with Amazon SageMaker AI
- 2.3Module 7: Evaluating and Tuning Machine Learning (ML) models Evaluating model performance Techniques to reduce training time Hyperparameter tuning techniques Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
- 2.4Module 8: Model Deployment Strategies Deployment considerations and target options Deployment strategies Choosing a model inference strategy Container and instance types for inference Lab 5: Shifting Traffic A/B
- Day 34
- 3.1Module 9: Securing AWS Machine Learning (ML) Resources Access control Network access controls for ML resources Security considerations for CI/CD pipelines
- 3.2Module 10: Machine Learning Operations (MLOps) and Automated Deployment Introduction to MLOps Automating testing in CI/CD pipelines Continuous delivery services Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio
- 3.3Module 11: Monitoring Model Performance and Data Quality Detecting drift in ML models SageMaker Model Monitor Monitoring for data quality and model quality Automated remediation and troubleshooting Lab 7: Monitoring a Model for Data Drift
- 3.4Module 12: Course Wrap-up
Instructor
