No-code Machine Learning and Generative AI - AWS

Objectives

• 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