With Amazon SageMaker Canvas, data and business analysts can prepare data, train, and deploy machine learning (ML) models without any ML experience or writing a single line of code. You will learn to build ML models for tabular and time series data without deep knowledge of ML. You can also practice what you learned in the course with the help of four labs.
You will also learn to use foundation models from Amazon and other model providers to support generative AI use cases such as text generation, text summarization, and chat using retrieval augmented generation (RAG).
After completing this course, you will be able to build and train highly accurate models, and generate predictions using batch inference. You will also be able to share models with data scientists for further analysis and deployment into your company’s ML operation pipelines.
- Course level: Intermediate
- Duration: 10.5 hours
Activities
This course includes eLearning interactions, knowledge checks, and follow-along demonstrations.
Course objectives
In this course, you will learn how to do the following:
- Describe basic machine learning (ML) concepts and techniques.
- Identify the ML life cycle and its phases.
- Describe the types of problems ML can solve.
- Identify the steps to building an ML model.
- Describe metrics for measuring the predictive accuracy of a model.
- Explain how to use Amazon SageMaker Canvas to transform raw data into a training dataset.
- Describe how to generate data insights and understand data quality.
- Identify how to find potential errors and extreme values in data with visualization tools.
- Describe the model building capabilities of SageMaker Canvas using AutoML.
- Use SageMaker Canvas to launch a model training job and track its progress.
- Describe the model quality metrics available in performance reports.
- Deploy a model and make predictions.
- Use the SageMaker Canvas foundational model (FM) user interface (UI) for text generation, text summarization, and model comparison.
- Identify and address challenges with foundation model outputs using RAG and fine-tuning.
- Describe best practices to follow when using Amazon SageMaker Canvas.
Intended audience
This course is intended for the following:
- Data analysts
- Researchers from non-ML domains
- Operations research analysts
- Junior data scientists
Prerequisites
We recommend that attendees of this course have:
- Experience with analysis, cleansing, and transforming tabular or time series data
- Basic understanding of statistical measures and regression
- AWS Technical Essentials course
For those new to generative AI, we recommend the following courses:
- Introduction to Generative AI - Art of the Possible
- Planning a Generative AI Project
Course outline
Module 1: Introduction to Machine Learning
How to Use This Course
ML Introduction
- ML Basics
- Types of Problems ML Can Solve
- ML Life Cycle
- Challenges with Processing and Deriving Insights from Data
Building and Evaluating Models
- Introduction to Model Building
- Model Evaluation
- Improving Model Performance
- Model Training Options
Wrap-up
- Knowledge Check
- Conclusion
Module 2: Data Analysis and Preparation
How to Use this Course
Introduction to Amazon SageMaker Canvas
- Amazon SageMaker Canvas
- Analyzing Data
- Quick Model Analysis
Data Preparation
- Transforming Data
- Exporting Data and Data Flows
Wrap-Up
- Knowledge Check
- Conclusion
Lab 1: Amazon SageMaker Canvas - Tabular Data Use Case
Lab 2: Amazon SageMaker Canvas - Time-Series Dataset Use Case
Module 3: Model Building Using SageMaker Canvas
How to Use this Course
Deep Dive on Sagemaker Canvas
- Introduction to Building a Model in SageMaker Canvas
- Advanced Options for Building Models in SageMaker Canvas
- Evaluating a Model in SageMaker Canvas
- Making Predictions and Deploying a Model in SageMaker Canvas
Wrap-Up
- Knowledge Check
- Conclusion
Lab 3: Build a Custom Model Using Amazon SageMaker Canvas
Lab 4: No-Code ML Capstone Lab
Module 4: Generative AI using SageMaker Canvas
How to Use this Course
Foundational Models in SageMaker Canvas
- Generative AI using Amazon SageMaker Canvas
- SageMaker Canvas Foundation Models
- Comparing Foundation Models
Mitigating Foundation Model Challenges in SageMaker Canvas
- Model Hallucinations
- Retrieval Augmented Generation (RAG)
- Fine-tuning Foundation Models
Wrap-Up
- Knowledge Check
- Conclusion
Module 5: Best Practices for SageMaker Canvas
How to Use this Course
Best Practices
- Getting Access to SageMaker Canvas
- Updating SageMaker Canvas Version
- Saving Costs with SageMaker Canvas
Wrap-Up
- Conclusion
Keywords
GenAI
Generative AI
