Welcome to Machine learning and generative AI on Amazon Sagemaker workshop
This workshops helps individuals customers and partners to learn about the fundamentals of machine learning and generative AI on Amazon SageMaker.
In this workshop, we’ll explore Amazon SageMaker robust tooling for core machine learning tasks such as data preparation, model building, training and deployment. We will also get hands on with Amazon SageMaker no-code/low-code generative AI interfaces as well as the fully managed generative AI AWS service Amazon Bedrock that allow anyone to easily integrate AI into their applications. The focus is on enabling hands-on experience with the platform through practical examples and coding tutorials for developers and data scientists of all levels.
Who should attend:
Data scientists, Analysts, ML engineer, Data Engineers and Developers who would like to learn about Machine Learning and gererative AI on Amazon SageMaker. Overview of the Labs
Hands-On:
Access to temporary AWS accounts will be provided for you on the day: No existing account required. For the best experience you may want to use a large screen or second screen if possible, to follow the workshop and hands-on side-by-side.
Prior Knowledge: Python is used as the programming language for all the labs and participants are assumed to have familiarity with Python.
Content of this workshop:
F1 Notebook: Lab1) SageMaker Studio Notebooks & Feature Engineering : learn about SageMaker notebooks and data explorations on Sagemaker
Lab 2)Train, Tune and Deploy model using SageMaker Built-in Algorithms: build, train and deploy a model
Lab3) Deploy one of the 10 000+ Hugging Face Transformers to Amazon SageMaker for Inference
F2- SageMaker AutoPilot: use SageMaker Autopilot to build and deploy a model
F3- Bring your own training script to train and deploy on SageMaker
Challenge Sklearn: this is challenge where you migrate a SKLearn model built in a local notebook and using Iris data into SageaMaker
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.