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Python ProgrammingPractical Data Science with Amazon SageMaker
Understand a day in the life of a data scientist from an expert AWS instructor
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Course : Practical Data Science with Amazon SageMaker
Level : Intermediate
Type : Live online or Classroom
Length :1 day
The Practical Science with Amazon SageMaker course will help you in your developer or DevOps engineer role understand the basics of ML and the steps involved in building ML models using Amazon SageMaker Studio. In this one-day, classroom training course an expert AWS instructor will walk you through how to prepare data and train, evaluate, tune, and deploy ML models.
Development operations (DevOps) engineers
Application developers
Basic understanding of Python programming
Basic knowledge of statistics
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 and train, evaluate and tune, and deploy ML models
And much more
Instructor-led Virtual online live Training.
Weekend Classes
Evening Classes
Weekdays Classes
Course Outline
Module 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
Amazon CodeWhisperer
Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
Hands-on Lab: Training a Model with Amazon SageMaker
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
Hands-on Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint
Best Value offer
Official Courseware
Instructor-led live online / Classroom
Flexible Schedule
Course Completion Certification
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