Machine Learning Engineering on AWS

• Explain machine learning fundamentals and their applications in the AWS Cloud.
• Process, transform, and engineer data for machine learning tasks using AWS services.
• Select appropriate machine learning algorithms and modeling approaches.
• Train, evaluate, and tune machine learning models using Amazon SageMaker AI.
• Deploy machine learning models using appropriate inference strategies.
• Design and implement scalable machine learning pipelines.
• Automate machine learning workflows using CI/CD.
• Apply security measures to machine learning resources on AWS.
• Monitor deployed models and detect data drift.
• Implement MLOps and automated deployment workflows.

Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS

Module 2: Analyzing Machine Learning (ML) Challenges

Module 3: Data Processing for Machine Learning (ML)

Module 4: Data Transformation and Feature Engineering

Module 5: Choosing a Modeling Approach

Module 6: Training Machine Learning (ML) Models

Module 7: Evaluating and Tuning Machine Learning (ML) Models

Module 8: Model Deployment Strategies

Module 9: Securing AWS Machine Learning (ML) Resources

Module 10: Machine Learning Operations (MLOps) and Automated Deployment

Module 11: Monitoring Model Performance and Data Quality

Module 12: Course Wrap-up

3 Days

Certification: AWS Certified Machine Learning Engineer – Associate

Current Exam: MLA-C01

Duration: 130 minutes

Questions: 65 questions

• 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
• Lab 3: Training a Model with Amazon SageMaker AI
• Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI
• Lab 5: Shifting Traffic A/B
• Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio
• Lab 7: Monitoring a Model for Data Drift

• Machine learning professionals
• ML engineers
• MLOps engineers
• Data engineers
• Data scientists
• Backend software developers
• DevOps professionals working with machine learning solutions

• Familiarity with basic machine learning concepts.
• Working knowledge of Python.
• Working knowledge of 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 is beneficial but not required.