Microsoft 
AWS 
EC-Council 
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Artificial Intelligence 
Python ProgrammingPractical Data Science with Amazon SageMaker
Understand a day in the life of a data scientist from an expert AWS instructor
Demand for AI Skills
Learning AI equips executives with the skills to navigate and lead in an increasingly digital business landscape.
Hands-On Learning
Understanding AI allows you to leverage technology to stay ahead of competitors and adapt to market changes.
Learn from AWS Experts
Learning AI equips executives with the skills to navigate and lead in an increasingly digital business landscape.
- Instructor-led
- AWS Official Courseware
- Flexible Schedule
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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
US$ 695
Buy now, pay later with PayPal
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Successfully pass the AIF-C01 in the first attempt
Knowledge Check
Question 1: Which AWS service is best suited for running large-scale distributed training of machine learning models?
- Amazon Polly
- AWS Batch
- Amazon SageMaker Training Jobs
- AWS Glue
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Sure, we would happy to assist you. Please direct your queries to info@infoventure.com
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