Curso The Machine Learning Pipeline on AWS

• Desarrolladores.
• Arquitectos de soluciones.
• Ingenieros de datos.
• Todo aquel con poca experiencia con ML o sin ella que desee aprender sobre la canalización de ML con Amazon SageMaker.

• Seleccionar y justificar el enfoque de ML adecuado para un problema de negocio determinado.
• Usar la canalización de ML para resolver un problema empresarial específico.
• Capacitar, evaluar y ajustar un modelo de ML en Amazon SageMaker.
• Describir algunas de las prácticas recomendadas para diseñar canalizaciones de ML escalables, optimizadas según el costo y seguras en AWS.
• Aplicar el aprendizaje automático a un problema comercial de la vida real después de completar el curso.

Módulo 0: Introduction

• Pre-assessment

Módulo 1: Introduction to Machine Learning and the ML Pipeline

• Overview of machine learning, including use cases, types of machine learning, and key concepts
• Overview of the ML pipeline
• Introduction to course projects and approach

Módulo 2: Introduction to Amazon SageMaker

• Introduction to Amazon SageMaker
• Demo: Amazon SageMaker and Jupyter notebooks
• Hands-on: Amazon SageMaker and Jupyter notebooks

Módulo 3: Problem Formulation

• Overview of problem formulation and deciding if ML is the right solution
• Converting a business problem into an ML problem
• Demo: Amazon SageMaker Ground Truth
• Hands-on: Amazon SageMaker Ground Truth
• Practice problem formulation
• Formulate problems for projects

Módulo 4: Preprocessing

• Overview of data collection and integration, and techniques for data preprocessing and visualization
• Practice preprocessing
• Preprocess project data
• Class discussion about projects

Módulo 5: Model Training

• Choosing the right algorithm
• Formatting and splitting your data for training
• Loss functions and gradient descent for improving your model
• Demo: Create a training job in Amazon SageMaker

Módulo 6: Model Evaluation

• How to evaluate classification models
• How to evaluate regression models
• Practice model training and evaluation
• Train and evaluate project models
• Initial project presentations

Módulo 7: Feature Engineering and Model Tuning

• Feature extraction, selection, creation, and transformation
• Hyperparameter tuning
• Demo: SageMaker hyperparameter optimization
• Practice feature engineering and model tuning
• Apply feature engineering and model tuning to projects
• Final project presentations

Módulo 8: Deployment

• How to deploy, inference, and monitor your model on Amazon SageMaker
• Deploying ML at the edge
• Demo: Creating an Amazon SageMaker endpoint
• Post-assessment
• Course wrap-up

Documentación oficial del curso The Machine Learning Pipeline on AWS.

• Formador certificado por AWS.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.