This hands-on workshop, aimed at engineering teams and solution builders, focuses on accelerating your machine learning journey so you can build, train and deploy high performing ML models quickly and at scale with Amazon SageMaker. Amazon SageMaker is a fully managed platform that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case.

Within this series of presentations & labs, you'll explore some of the most common ML-in-production patterns our customers are leveraging with Amazon SageMaker. We will demonstrate techniques for bringing your own models (scripts and containers) to the cloud, implementing MLOps following best practices, and deploying models for inference. At the end of the workshop, you will have gained hands-on experience implementing these patterns using Amazon SageMaker to replicate this in your own environment.

The workshop is designed to run over a period of 4.5 hours, including breaks in between, providing ample time to delve into these machine learning use cases on AWS. Whether you're a startup founder, CTO, ML engineer, or developer, this workshop promises to be an invaluable learning experience. Through hands-on exercises and expert guidance from our experienced AWS Solutions Architects in the AI/ML field, you'll gain the skills to leverage Amazon SageMaker effectively.

Agenda

1. Bring Your Own Model (BYOM): with SageMaker you have the flexibility to bring your own model and leverage the capabilities of the service. Learn how you can leverage Amazon SageMaker to train and deploy your own model cost efficiently and at scale.

2. Amazon SageMaker MLOps: learn how MLOps can streamline your end-to-end ML lifecycle and how Amazon SageMaker purpose-built tools and built-in integrations with other AWS service can accelerate the adoption of MLOps across your company.

3. ML Inference with Amazon SageMaker: discover how you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden with Amazon SageMaker’s broad selection of ML infrastructure and model deployment option.