Amazon SageMaker is a fully managed service that allows developers and data scientists to build, train, and deploy machine learning models. There are a lot of components to SageMaker, whether you’re using the managed development environments, the ephemeral training clusters, the hyperparamater tuning, or the deployed endpoints among lots of different features and capabilities.
We typically talk about those capabilities as falling into four categories: Data preparation, the model build phase, training and tuning, and deployment and management (or hosting).
These four categories really address the needs that ML builders have when dealing with each stage of a model’s lifecycle.
During this session, I will walk you through on the 4 key categories and give you a full feature tour of as many of these features that might be interesting to you.
Session delivered at the APN AI/ML Blackbelt track for AWS partners in Nov, 2021.
- Amazon Sagemaker - Machine Learning for every data scientist and developer ( Download)
- Introduction to Amazon SageMaker ( Download)
- Masterclass: Amazon SageMaker - Machine Learning For Every Data Scientist And Developer ( Download)
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- Introduction to Amazon SageMaker Studio | Amazon Web Services ( Download)
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- Modernize your machine learning development using Amazon SageMaker ( Download)
- I'm a Data Scientist - Automatically Build Models With Amazon Sagemaker (Level 400) ( Download)
- An Overview of AI and Machine Learning Services From AWS ( Download)
- Build, Train and Deploy ML Models at Scale with Amazon SageMaker ( Download)
- Getting Started with Amazon SageMaker - Build, Train, and Deploy Machine Learning Models ( Download)
- Introduction and Demo of Machine Learning using Amazon SageMaker by Paul Colmer ( Download)
- Quick Introduction to Amazon SageMaker ( Download)
- How to Use Automated Machine Learning with Amazon SageMaker Autopilot ( Download)
- Data Science using SageMaker - Machine Learning in Cloud ( Download)