Become the Maestro of your MLOps Abstractions

The MLOps ecosystem is evolving into a sophisticated symphony, composed of diverse tools, methodologies, and cultures. This diversity, while beneficial, also introduces a complexity reminiscent of the challenges encountered in Big Data systems. Data experts had to navigate through immense data... View article

Towards AGI: Making LLMs Better at Reasoning (1/2)

Techniques to make LLMs proficient in math and symbolic reasoning – by a former ML Engineer In a latest news by Reuters, several OpenAI staff researchers wrote a letter to the board of directors warning them of a powerful AI discovery (potential... View article

AI Tidbits 2023 SOTA Report

Looking back at 2023’s advancements to gauge how far we’ve come since 2022 Note: “SOTA” stands for state-of-the-art, referring to the most advanced and effective models currently available in the field. Exactly a year ago, ChatGPT was one month old,... View article

How to Build a Knowledge Assistant at Scale

Introduction The discussion about the myriad applications of Large Language Models (LLMs) is extensive and well-trodden in tech circles1. These models have opened many use cases, reshaping sectors from customer service to content creation. However, an often-overlooked aspect in this... View article

LLMOps: Why Does It Matter?

MLOps has become a popular term in the context of machine learning pipelines. It refers to the various operations involved, from building models to deploying them in real-world settings. Its goal is to ensure that machine learning processes are reproducible,... View article

Back from Apply(ops) 23 conference

In mid-November, the Apply(ops) 23 conference, organized by Tecton and Demetrios Brinkmann, took place. Usually, I write a one-page summary for my teammates to share learnings and good references, which I also post on LinkedIn. However, this time, I decided to write... View article

Competitive Differentiation for Foundation Models in the LLM Space

Compute Performance, Safety and Alignment, Accuracy and Retrieval Augmented Generation are Three Emerging Differentiation Vectors Machine Learning foundation models are a new category that has largely been undifferentiated – the major providers have been competing on similar types of customer... View article