Are you a startup founder or machine learning (ML) engineer looking to effectively deploy and manage AI models while optimizing costs?

Join us for an intensive hands-on workshop exploring Amazon SageMaker Studio's unified ML development environment and learn production-ready strategies for model deployment.

DeepSeek is a cutting-edge family of large language models that has gained significant attention in the AI community for its impressive performance, cost-effectiveness, and open-source nature. DeepSeek offers a range of models including the powerful DeepSeek-V3, the reasoning-focused DeepSeek-R1, and various distilled versions. These models stand out for their innovative architecture, utilizing techniques like Mixture-of-Experts and Multi-Head Latent Attention to achieve high performance with lower computational requirements.

In this hands-on workshop, you'll learn about Amazon SageMaker Studio's comprehensive toolkit to self-host large language models from DeepSeek while maintaining cost efficiency.

Who is this for? This workshop is ideal for:

• Startup founders and technical leaders creating AI solutions
• ML Engineers and Data Scientists
• DevOps professionals managing GenAI/ML infrastructure
• Technical decision-makers evaluating GenAI/ML platforms
• Developers interested in self-hosting open-source LLMs
• Engineers looking to optimize their GenAI/ML infrastructure costs

During this hands-on workshop, you'll learn how to leverage Amazon SageMaker Studio's unified environment to streamline your ML workflows and implement cost-effective model deployment strategies.

Key highlights:

• Master Amazon SageMaker Studio's unified interface and development environment
• Hands-on implementation of self-hosting DeepSeek and similar models
• Deploy cost-optimization strategies including scale-to-zero capabilities
• Enhance inference performance using Fast Model Loader and container caching
• Best practices for managing GenAI/ML development lifecycle
• Real-world examples of production GenAI/ML infrastructure optimization
• Interactive troubleshooting and optimization sessions

This workshop is specifically designed for startup teams who want to productionze GenAI/ML infrastructure while maintaining cost efficiency. You'll gain hands-on experience with Amazon SageMaker's advanced features and learn practical strategies for managing GenAI/ML workloads.

Prerequisites:

• Laptop with adequate specifications for hands-on exercises
• Basic understanding of machine learning concepts
• Familiarity with Python programming
• AWS account access
• Basic knowledge of container technologies
• Understanding of ML deployment concepts.

By registering, you agree to the AWS Event Terms & Conditions and AWS Code of Conduct.