Practical Data Science with Amazon SageMaker
- Código del Curso GK0630
- Duración 1 Día
- Versión 2.0.10
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Método de Impartición
Este curso está disponible en los siguientes formatos:
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Cerrado
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Clase de calendario
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Aprendizaje Virtual
Aprendizaje virtual
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Temario
Parte superiorArtificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.
Course level: Intermediate
Duration: 1 day
Activities
This course includes presentations, hands-on labs, and demonstrations.
Curso Remoto (Abierto)
Nuestra solución de formación remota o virtual, combina tecnologías de alta calidad y la experiencia de nuestros formadores, contenidos, ejercicios e interacción entre compañeros que estén atendiendo la formación, para garantizar una sesión formativa superior, independiente de la ubicación de los alumnos.
Calendario
Parte superiorDirigido a
Parte superior- Development Operations (DevOps) engineers
- Application developers
Objetivos del Curso
Parte superiorIn this course, you will learn to:
- Discuss the benefits of different types of machine learning for solving business problems
- Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
- Explain how data scientists use AWS tools and ML to solve a common business problem
- Summarize the steps a data scientist takes to prepare data
- Summarize the steps a data scientist takes to train ML models
- Summarize the steps a data scientist takes to evaluate and tune ML models
- Summarize the steps to deploy a model to an endpoint and generate predictions
- Describe the challenges for operationalizing ML models
- Match AWS tools with their ML function
Contenido
Parte superiorModule 1: Introduction to Machine Learning
- Benefits of machine learning (ML)
- Types of ML approaches
- Framing the business problem
- Prediction quality
- Processes, roles, and responsibilities for ML projects
Module 2: Preparing a Dataset
- Data analysis and preparation
- Data preparation tools
- Demonstration: Review Amazon SageMaker Studio and Notebooks
- Hands-On Lab: Data Preparation with SageMaker Data Wrangler
Module 3: Training a Model
- Steps to train a model
- Choose an algorithm
- Train the model in Amazon SageMaker
- Hands-On Lab: Training a Model with Amazon SageMaker
- Amazon CodeWhisperer
- Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
Module 4: Evaluating and Tuning a Model
- Model evaluation
- Model tuning and hyperparameter optimization
- Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker
Module 5: Deploying a Model
- Model deployment
- Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction
Module 6: Operational Challenges
- Responsible ML
- ML team and MLOps
- Automation
- Monitoring
- Updating models (model testing and deployment)
Module 7: Other Model-Building Tools
- Different tools for different skills and business needs
- No-code ML with Amazon SageMaker Canvas
- Demonstration: Overview of Amazon SageMaker Canvas
- Amazon SageMaker Studio Lab
- Demonstration: Overview of SageMaker Studio Lab
- (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint
Pre-requisitos
Parte superiorWe recommend that attendees of this course have:
- AWS Technical Essentials
- Entry-level knowledge of Python programming
- Entry-level knowledge of statistics