Practical Data Science with Amazon SageMaker
In this intermediate-level course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.
Objetivos
Prepare a dataset for training
Train and evaluate a Machine Learning model
Automatically tune a Machine Learning model
Prepare a Machine Learning model for production
Think critically about Machine Learning model results
Cloud computing
Disponible en formato e-learning
Disponible en formato presencial
Disponible en formato a distancia
Subvención disponible
A través de Fundae, cumpliendo requisitos.
Duración
5 horas
- Dificultad 50%
- Nivel alcanzado 80%
Dirigido a
This course is intended for:
Developers
Data Scientists
Conocimientos requeridos
Familiarity with Python programming language
Basic understanding of Machine Learning
Temario
Day One
Module 1: Introduction to Machine Learning
Types of ML
Job Roles in ML
Steps in the ML pipeline
Module 2: Introduction to Data Prep and SageMaker
Training and Test dataset defined
Introduction to SageMaker
Demo: SageMaker console
Demo: Launching a Jupyter notebook
Module 3: Problem formulation and Dataset Preparation
Business Challenge: Customer churn
Review Customer churn dataset
Module 4: Data Analysis and Visualization
Demo: Loading and Visualizing your dataset
Exercise 1: Relating features to target variables
Exercise 2: Relationships between attributes
Demo: Cleaning the data
Module 5: Training and Evaluating a Model
Types of Algorithms
XGBoost and SageMaker
Demo 5: Training the data
Exercise 3: Finishing the Estimator definition
Exercise 4: Setting hyperparameters
Exercise 5: Deploying the model
Demo: Hyperparameter tuning with SageMaker
Demo: Evaluating Model Performance
Module 6: Automatically Tune a Model
Automatic hyperparameter tuning with SageMaker
Exercises 6-9: Tuning Jobs
Module 7: Deployment / Production Readiness
Deploying a model to an endpoint
A/B deployment for testing
Auto Scaling Scaling
Demo: Configure and Test Autoscaling
Demo: Check Hyperparameter tuning job
Demo: AWS Autoscaling
Exercise 10-11: Set up AWS Autoscaling
Cost of various error types
Demo: Binary Classification cutoff
Module 9: Amazon SageMaker Architecture and features
Accessing Amazon SageMaker notebooks in a VPC
Amazon SageMaker batch transforms
Amazon SageMaker Ground Truth
Amazon SageMaker Neo
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