Practical Data Science with Amazon SageMaker
- Código del Curso GK0630
- Duración 1 Día
- Versión 2.0.10
Otros Métodos de Impartición
Método de Impartición
Este curso está disponible en los siguientes formatos:
-
Clase de calendario
Aprendizaje tradicional en el aula
-
Aprendizaje Virtual
Aprendizaje virtual
Solicitar este curso en un formato de entrega diferente.
Temario
Parte superiorCalendario
Parte superiorDirigido a
Parte superiorThis course is intended for:
- Developers
- Data Scientists
Objetivos del Curso
Parte superiorIn this course, you will learn how to:
- 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
Contenido
Parte superiorDay 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
Pre-requisitos
Parte superior- Familiarity with Python programming language
- Basic understanding of Machine Learning
- /es-es/-/media/global-knowledge/merchandising/right-side-column/es/250x600--training-subscriptions_es.jpg https://www.globalknowledge.com/es-es/training/suscripciones/gk-polaris?utm_source=website&utm_medium=banner&utm_campaign=webbanner #000000
- #000000
- GK0630
- Practical Data Science with Amazon SageMaker
- Big Data
- GK0630 | Practical Data Science with Amazon SageMaker | Training Course | Amazon Web Services.
- Amazon Web Services