Machine Learning Engineering on AWS
In this course you will learn how to learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.
Nästa start: 24 aug. (Live Online)
Om utbildningen
You 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.
Target audience and prerequisites
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.
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
Detaljerad information
Module 1: Introduction to Machine Learning (ML) on AWS
- Topic 1A: Introduction to ML
- Topic 1B: Amazon SageMaker AI
- Topic 1C: Responsible ML
Module 2: Analyzing Machine Learning (ML) Challenges
- Topic 2A: Evaluating ML business challenges
- Topic 2B: ML training approaches
- Topic 2C: ML training algorithms
Module 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 storage
Module 4: Data Transformation and Feature Engineering
- Topic 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 SDK
Module 5: Choosing a Modeling Approach
- Topic 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 considerations
Module 6: Training Machine Learning (ML) Models
- Topic 6A: Model training concepts
- Topic 6B: 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 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 AI
Module 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/B
Module 9: Securing AWS Machine Learning (ML) Resources
- Topic 9A: Access control
- Topic 9B: Network access controls for ML resources
- Topic 9C: Security considerations for CI/CD pipelines
Module 10: Machine Learning Operations (MLOps) and Automated Deployment
- Topic 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 Studio
Module 11: Monitoring Model Performance and Data Quality
- Topic 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 Drift
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