Practical Data Science with Amazon SageMaker -1 Day- Instructor Led

Unlock the power of machine learning and elevate your business with our hands-on course! Discover the benefits of different ML types and learn how data scientists leverage AWS tools to solve real-world business problems. From preparing data to training, evaluating, and tuning models, you’ll gain a comprehensive understanding of the entire ML pipeline. Explore the roles and responsibilities of ML teams, tackle the challenges of operationalizing models, and gain the expertise to deploy ML solutions that generate actionable predictions. Equip yourself with the knowledge to choose the right AWS tools for every stage of your ML journey. Enroll now and start transforming your business with AI-driven insights!

This course includes presentations, hands-on labs, and demonstrations.

In 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

This course is intended for:

• Development Operations (DevOps) engineers

• Application developers

We recommend that attendees of this course have:

• AWS Technical Essentials

• Entry-level knowledge of Python programming

• Entry-level knowledge of statistics

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

• 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