MLEAAWAmazon Web ServicesAW-MLEA1.0<p>In this course, you will learn to do the following:</p> <ul> <li>Explain ML fundamentals and its applications in the AWS Cloud.</li><li>Process, transform, and engineer data for ML tasks by using AWS services.</li><li>Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.</li><li>Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.</li><li>Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.</li><li>Discuss appropriate security measures for ML resources on AWS.</li><li>Implement monitoring strategies for deployed ML models, including techniques for detecting data drift.</li></ul><p>We recommend that attendees of this course have the following:</p> <ul> <li>Familiarity with basic machine learning concepts</li><li>Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn</li><li>Basic understanding of cloud computing concepts and familiarity with AWS</li><li>Experience with version control systems such as Git (beneficial but not required)</li></ul><p>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.</p><ul> <li>Course Introduction</li><li>Introduction to Machine Learning (ML) on AWS</li><li>Analyzing Machine Learning (ML) Challenges</li><li>Data Processing for Machine Learning (ML)</li><li>Data Transformation and Feature Engineering</li><li>Choosing a Modeling Approach</li><li>Training Machine Learning (ML) Models</li><li>Evaluating and Tuning Machine Learning (ML) models</li><li>Model Deployment Strategies</li><li>Securing AWS Machine Learning (ML) Resources</li><li>Machine Learning Operations (MLOps) and Automated Deployment</li><li>Monitoring Model Performance and Data Quality</li><li>Course Wrap-up</li></ul><h5>Module 0: Course Introduction</h5><h5>Module 1: Introduction to Machine Learning (ML) on AWS</h5><ul> <li>Topic A: Introduction to ML</li><li>Topic B: Amazon SageMaker AI</li><li>Topic C: Responsible ML</li></ul><h5>Module 2: Analyzing Machine Learning (ML) Challenges</h5><ul> <li>Topic A: Evaluating ML business challenges</li><li>Topic B: ML training approaches</li><li>Topic C: ML training algorithms</li></ul><h5>Module 3: Data Processing for Machine Learning (ML)</h5><ul> <li>Topic A: Data preparation and types</li><li>Topic B: Exploratory data analysis</li><li>Topic C: AWS storage options and choosing storage</li></ul><h5>Module 4: Data Transformation and Feature Engineering</h5><ul> <li>Topic A: Handling incorrect, duplicated, and missing data</li><li>Topic B: Feature engineering concepts</li><li>Topic C: Feature selection techniques</li><li>Topic D: AWS data transformation services</li><li>Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR</li><li>Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK</li></ul><h5>Module 5: Choosing a Modeling Approach</h5><ul> <li>Topic A: Amazon SageMaker AI built-in algorithms</li><li>Topic B: Amazon SageMaker Autopilot</li><li>Topic C: Selecting built-in training algorithms</li><li>Topic D: Model selection considerations</li><li>Topic E: ML cost considerations</li></ul><h5>Module 6: Training Machine Learning (ML) Models</h5><ul> <li>Topic A: Model training concepts</li><li>Topic B: Training models in Amazon SageMaker AI</li><li>Lab 3: Training a model with Amazon SageMaker AI</li></ul><h5>Module 7: Evaluating and Tuning Machine Learning (ML) models</h5><ul> <li>Topic A: Evaluating model performance</li><li>Topic B: Techniques to reduce training time</li><li>Topic C: Hyperparameter tuning techniques</li><li>Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI</li></ul><h5>Module 8: Model Deployment Strategies</h5><ul> <li>Topic A: Deployment considerations and target options</li><li>Topic B: Deployment strategies</li><li>Topic C: Choosing a model inference strategy</li><li>Topic D: Container and instance types for inference</li><li>Lab 5: Shifting Traffic</li></ul><h5>Module 9: Securing AWS Machine Learning (ML) Resources</h5><ul> <li>Topic A: Access control</li><li>Topic B: Network access controls for ML resources</li><li>Topic C: Security considerations for CI/CD pipelines</li></ul><h5>Module 10: Machine Learning Operations (MLOps) and Automated Deployment</h5><ul> <li>Topic A: Introduction to MLOps</li><li>Topic B: Automating testing in CI/CD pipelines</li><li>Topic C: Continuous delivery services</li><li>Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio</li></ul><h5>Module 11: Monitoring Model Performance and Data Quality</h5><ul> <li>Topic A: Detecting drift in ML models</li><li>Topic B: SageMaker Model Monitor</li><li>Topic C: Monitoring for data quality and model quality</li><li>Topic D: Automated remediation and troubleshooting</li><li>Lab 7: Monitoring a Model for Data Drift</li></ul><h5>Module 12: Course Wrap-up</h5>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.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)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 Introduction - Introduction to Machine Learning (ML) on AWS - Analyzing Machine Learning (ML) Challenges - Data Processing for Machine Learning (ML) - Data Transformation and Feature Engineering - Choosing a Modeling Approach - Training Machine Learning (ML) Models - Evaluating and Tuning Machine Learning (ML) models - Model Deployment Strategies - Securing AWS Machine Learning (ML) Resources - Machine Learning Operations (MLOps) and Automated Deployment - Monitoring Model Performance and Data Quality - Course Wrap-upModule 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 Module 5: Choosing a Modeling Approach - Topic A: Amazon SageMaker AI built-in algorithms - Topic B: Amazon SageMaker Autopilot - Topic C: Selecting built-in training algorithms - 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 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-up3 days1995.001995.002470.001995.001995.002025.002795.002655.003500.002450.001650.002850.002850.002395.00