In-person Workshop: Build and deploy LLM-based applications

Thursday, June 15, 10:30AM UTC

LLMs have gained immense popularity in recent months. An entirely new ecosystem of pre-trained models and tools has emerged that streamline the process of building LLM-based applications.

Join us for this in-person workshop in San Francisco, CA to experience building scalable LLM-based applications hands-on! Coffee and snacks included.

Key Takeaways

  1. Use libraries like Ray, HuggingFace, and LangChain to build LLM-based applications based on open-source code, models, and data.

  2. Learn about scaling LLM fine-tuning and inference, along with trade-offs.

  3. Use embedding models and vector stores.

  4. Learn about using modern deployment tools to run your application online and continually improve it.


  1. This workshop is for LLM-based application builders as well as ML practitioners interested in how to build LLM-based applications using open source tools.

  2. Basic understanding of emerging LLM trends is not necessary, but will help get more out of the workshop.




    blog posts are helpful background material.


  1. GitHub repository with relevant resources including notebooks, setup instructions, bonus resources, and a README for an overview.

  2. Access to the GPU-based compute cluster for the duration of the training to leverage the full potential of LLMs.

  3. Exclusive only to the workshop participants: office hours the week after the workshop for deeper discussion of the individual application ideas.


Emmy Headshot

Emmy Li

Technial Trainer

Emmy is a technical trainer at Anyscale Inc. She holds a B.Sc in Physics from Stanford University where she contributed toward computational astrophysics research at the Stanford Linear Accelerator Laboratory and NASA’s Jet Propulsion Laboratory.

Emmy is passionate about creating high quality educational materials and sharing them with the broader Ray community.

Adam Briendel's Headshot

Adam Breindel

Technical Instructor

Adam Breindel is a member of the Anyscale training team and he consults and teaches on large-scale data engineering and AI/machine learning. He has served as technical reviewer for numerous O'Reilly titles covering Ray, Apache Spark, and other topics.

Adam's 20 years of engineering experience include numerous startups and large enterprises with projects ranging from AI/ML systems and cluster management to web, mobile, and IoT apps.

He holds a BA (Mathematics) from University of Chicago and a MA (Classics) from Brown University. Adam's interests include hiking, literature, and complex adaptive systems.