Token 1.21: Vulnerabilities in LLMs and how to deal with them

Token 1.20: Explainable AI techniques and tools for LLMs

that will help you decipher these models and their predictions

Token 1.19: How to optimize LLM Inference

Tools, Techniques, and Hardware Solutions

Token 1.18: How to Monitor LLMs?

Ensuring Your LLMs Deliver Real Value

Token 1.17: Deploying ML Model: Best practices feat. LLMs

Unless you are a researcher whose sole job is to beat benchmarks at some predefined dataset, you will want to deploy your model

Token 1.14: What is Synthetic Data and How to Work with it?

Will it eliminate the need for real data? Let's explore

Token 1.13: Where to Get Data for Data-Hungry Foundation Models

Explore how data is gathered to train FMs, learn a few data efficient training techniques and the ethics of data