This course covers a deep dive into Amazon SageMaker, focusing on various aspects such as managing notebook instances, utilizing built-in machine learning algorithms, training and deploying ML models, tuning model accuracy, distributed training, feature engineering, batch transformations, and more. The course aims to teach learners how to work with SageMaker effectively, enabling them to build, train, deploy, and optimize machine learning models for real-world applications. The intended audience for this course includes data scientists, machine learning engineers, AI developers, and anyone interested in leveraging Amazon SageMaker for machine learning projects.
Overview
Syllabus
Fully-Managed Notebook Instances with Amazon SageMaker - a Deep Dive.
Built-in Machine Learning Algorithms with Amazon SageMaker - a Deep Dive.
Bring Your Own Custom ML Models with Amazon SageMaker.
Train Your ML Models Accurately with Amazon SageMaker.
Deploy Your ML Models to Production at Scale with Amazon SageMaker.
Tune Your ML Models to the Highest Accuracy with Amazon SageMaker Automatic Model Tuning.
Scale up Training of Your ML Models with Distributed Training on Amazon SageMaker.
Use the Deep Learning Framework of Your Choice with Amazon SageMaker.
Learn to Analyze the Co-Relation in Your Datasets Using Feature Engineering with Amazon SageMaker.
Get Scheduled Predictions on Your ML Models with Amazon SageMaker Batch Transform.
Build Highly Accurate Training Datasets at Reduced Costs with Amazon SageMaker Ground Truth.
Organize, Track, and Evaluate ML Training Runs With Amazon SageMaker Experiments.
Automatically Build, Train, and Tune ML Models With Amazon SageMaker Autopilot.
Deploy Multiple ML Models on a Single Endpoint Using Multi-model Endpoints on Amazon SageMaker.
Amazon SageMaker Studio - A Fully Integrated Development Environment For Machine Learning.
Analyze, Detect, and Get Alerted on Problems With Training Runs Using Amazon SageMaker Debugger.
Taught by
Amazon web services
