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

A Halfway Recap – Decoding FMOps: From Basics to Advanced RAG and CoT

Organizing emerging knowledge in real-time

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

Token 1.12: What is Vector Database's Role in FMOps?

We explain what vector databases are and how they work, explore alternative solutions and provide expert insight on security

Token 1.11: What is Low-Rank Adaptation (LoRA)?

Making fine-tuning more efficient and less costly

Token 1.10: Large vs Small in AI: The Language Model Size Dilemma

Your Guide to the Pros and Cons of Large and Small AI Models

Token 1.9: Open- vs Closed-Source AI Models: Which is the Better Choice for Your Business?

A Deep Dive into the Pros and Cons of Each Approach with a Few Useful Tips

Token 1.8: Silicon Valley of AI Chips and Semiconductors

Understanding the Chips that Drive Today's AI Breakthroughs

Token 1.7: What Are Chain-of-Verification, Chain of Density, and Self-Refine?

Your Expert Guide to Seminal Concepts in AI Chaining

Token 1.6: Transformer and Diffusion-Based Foundation Models

the main things you need to know about frontrunners in the GenAI space

Token 1.5: From Chain-of-Thoughts to Skeleton-of-Thoughts, and everything in between

How to distinguish all the СoT-inspired concepts and use them for your projects

Token 1.4: Foundation Models – The Building Blocks

We touch upon some systematic concepts and also offer a few practical insights from Rishi Bommasani

Token 1.3: What is Retrieval-Augmented Generation (RAG)?

we discuss the origins of RAG, what LLMs limitations it tries to fix, its architecture, and why it is so popular. Enjoy the collection of helpful links