Understanding Retrieval Augmented Generation (RAG): Supercharging LLM Capabilities with Embeddings and Semantic Search
Learn how to leverage Vector DBs and RAG for supercharging your LLM knowledge.
Rafael Pierre
Rafael has a track record of 15 years spanning software engineering, AI and solution architecture. He currently works as an ML & Software Engineer at Hugging Face. Before that, he started weet.ai to help customers unlock business value from Data and AI by leveraging his knowledge in MLOps, Deep Learning, Computer Vision, Large Language Models and Generative AI.
Large Language Models and No Code Tooling: A Match Made in Heaven?
In this blog we provide an overview on some no code tools and frameworks for LLMs, Prompt Engineering, Agents and LangChain.
Surviving the LLM Jungle: When to use Prompt Engineering, Retrieval Augmented Generation or Fine Tuning?
In this blog, we explore three key strategies for harnessing the power of LLMs: Prompt Engineering, Retrieval Augmented Generation, and Fine Tuning.
Webinar: Building an LLMOps Stack for Large Language Models
Learn how to use different components in an LLMOps stack to make sure your LLMs investmet doesn't go down the drain.
Parameter-Efficient Fine-Tuning (PEFT): Enhancing Large Language Models with Minimal Costs
PEFT is the easiest way to optimise costs when fine tuning Large Language Models (LLMs). Learn more!
Keeping Your Machine Learning Models on the Right Track: Getting Started with MLflow, Part 2
A deeper dive into how to use MLflow for streamlining your MLOps best practices.
Keeping Your Machine Learning Models on the Right Track: Getting Started with MLflow, Part 1
MLflow is the MLOps standard for tracking ML experiments and models. Learn how to get started.
What is Machine Learning Drift, and How it Can Negatively Affect Your Machine Learning Investment
Learn what is Machine Learning Drift and how to avoid it.