The Machine Learning Pipeline on AWS

• 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

• Conocimientos básicos del lenguaje de programación Python. 
• Comprensión básica de la infraestructura de la nube de AWS (Amazon S3 y Amazon CloudWatch). 
• Experiencia básica de trabajo en un entorno de bloc de notas de Jupyter

Modulo 0: Introduction

• Pre-assessment

Modulo 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

Modulo 2: Introduction to Amazon SageMaker

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

Modulo 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

Modulo 4: Preprocessing

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

Modulo 5: Model Training

• Choosing the right algorithm
• Formatting and splitting your data for training
• Loss functions and gradient descent for improving your model

Modulo 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

Modulo 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

Modulo 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 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