FMOps

Recap#2: How does FMOps Infrastructure Stack look like?

+ our best explanatory tokens about it, including a list of open-source tools, libraries, and companies

Recap#1 of FMOps: key concepts, techniques, and resources

Systematizing the knowledge about foundation models that are the backbone of generative AI

Token 1.24: Understanding Multimodal Models

and what so special about them in 2024

Token 1.23: Mitigating Bias in Foundation Models/LLMs

Your guide on how to identify bias, a few debiasing techniques, and collection of tools and libraries for detection and mitigation

Token 1.22: Data Privacy in LLM systems

From concerns about data leakage to the main technical strategies for managing privacy in LLMs.

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 1.16: Understanding RLHF and its use cases

Deep dive into a groundbreaking alignment technique with a lot of potential

Token 1.15: What are Hallucinations: a Critical Challenge or an Opportunity?

We explore why hallucinations occur, strategies and methods for identifying them, and if they can be beneficial + a curated list of datasets, libraries, and tools