FourthBrain Workshop
Proprietary Data and LLMs
Build a data-secure application
An advanced workshop for ML engineers, tech leads and managers on using LLMs with proprietary data. You’ll learn to leverage LLMs for enterprise applications using sensitive data and domain-specific requirements.
- Date: Friday, December 15th
- Time: 8am-2:30pm PT
- Cost: $500 for individuals | $1,500 for teams of 4
Building an enterprise application using current available LLMs can introduce security and privacy risks. Maintaining data privacy is a crucial step while building generative AI applications.
In this workshop you will learn to build and deploy an LLM-powered application while maintaining data privacy. You’ll learn to host your own LLMs, fine-tune them for domain-specific and privacy-sensitive enterprise use cases, and explore the best evaluation methods to validate and monitor performance.
Key Outcomes
Understand strategies for preserving data privacy and security
Adapt pre-trained models to specific industries and domains
Evaluate and deploy fine-tuned models effectively
Workshop Schedule
This workshop includes one 6-hour live class session.
- Overview of LLMs and the current tech landscape
- Guidance for when fine-tuning is appropriate (or not!)
- Strategies for preserving data privacy in fine-tuning (federated p-tuning, differential privacy, etc.)
Download the Syllabus
This workshop is for you if:
1
You are an AI or ML engineer working on generative AI applications using proprietary or sensitive data
2
You have some familiarity with deep learning concepts; intermediate knowledge of Natural Language Processing is recommended.
3
You are fluent in Python and have ML and deployment experience
How to Prepare
Many employers offer reimbursement for programs like ours. Check out our tips for getting reimbursed.
Register Here
FAQs
About FourthBrain
FourthBrain's mission is to bring more people into the growing fields of Machine Learning and Artificial Intelligence through flexible education programs. We equip leaders with the skills to lead organizations towards AI maturity, and support engineers, developers, and data scientists to make an impact in this field.