ANZ-Amazon-SageMaker-ImmersionDay-workshop
Welcome to Machine learning with amazon sagemaker workshop
This workshops help customers and partners to learn about the fundamentals of machine learning on amazon SageMaker.
In this hands-on session, we'll introduce Amazon SageMaker with a focus on the core workflow of data processing, building, training and deploying models cost-effectively. Attendees will learn how to map traditional, notebook-based or local ways of working to SageMaker patterns - and explore some of the initial lineage tracking and infrastructure optimization benefits that SageMaker's architecture delivers.
Who should attend:
Data scientists, Analysts, ML engineer, Data Engineers and Developers who would like to learn about Machine Learning 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- 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
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
Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.