Inaem

Finalidad

Formar de manera adecuada a profesionales del sector (tic o audiovisual) para su inmediata adecuación laboral

Programa

Módulo 1: Course Introduction
Módulo 2: Introduction to Machine Learning (ML) on AWS
- Introduction to ML
- Amazon SageMaker AI
- Responsible ML
Módulo 3: Analyzing Machine Learning (ML) Challenges
- Evaluating ML business challenges
- ML training approaches
- ML training algorithms
Módulo 4: Data Processing for Machine Learning (ML)
- Data preparation and types
- Exploratory data analysis
- AWS storage options and choosing storage
Módulo 5: 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
Módulo 6: Choosing a Modeling Approach
- Amazon SageMaker AI built-in algorithms
- Selecting built-in training algorithms
- Amazon SageMaker Autopilot
- Model selection considerations
- ML cost considerations
Módulo 7: Training Machine Learning (ML) Models
- Model training concepts
- Training models in Amazon SageMaker AI
- Lab 3: Training a model with Amazon SageMaker AI
Módulo 8: 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
Módulo 9: 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
Módulo 10: Securing AWS Machine Learning (ML) Resources
- Access control
- Network access controls for ML resources
- Security considerations for CI/CD pipelines
Módulo 11: 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
Módulo 12: 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
Módulo 13: Course Wrap-up

Profesorado

PROFESORADO ESPECIALIZADO

Equipamiento

EQUIPAMIENTO PLATAFORMA CTA