LLM Avalanche
At the end of June, I flew out to San Francisco to do three things: I want to break down LLM Avalanche. Aside from being basically a mini-conference that we called a meetup, there were incredible learnings. It would be... View article
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Three email templates to ask your boss to pay for your continued learning
Dear boss, You may have noticed that I have been performing on an elevated level since [date you joined the community]. Some in the organization have even gone as far as to say I am carrying the team. We don't need to get into specifics. That's not what this correspondence is about.... View article
MLOps: More Oops than Ops
As model complexity increases exponentially, so too does the need for effective MLOps practices. This post acts as a transparent write-up of all the MLOps frustrations I’ve experienced in the last few days. By sharing my challenges and insights, I... View article
Building the Future with LLMOps: The Main Challenges
The following is an extract from Andrew McMahon’s book, Machine Learning Engineering withPython, Second Edition. Available on Amazon at https://packt.link/w3JKL. Given the rise in interest in LLMs recently, there has been no shortage of people expressing the desire to integrate... View article
Finding Harmony in MLOps: Balancing Functional and Object-Oriented Approaches ☯
Programmers have always been passionate about their preferences, whether they discuss spaces vs. tabs, Vim vs. Emacs, or light mode vs. dark mode. These debates have withstood the test of time, indicating that there is a place for each solution, and no definitive... View article
Explainable AI: Visualizing Attention in Transformers
And logging the results in an experiment-tracking tool In this article, we explore one of the most popular tools for visualizing the core distinguishing feature of transformer architectures: the attention mechanism. Keep reading to learn more about BertViz and how... View article
Evaluating and Debugging Generative AI : A Deep Dive Into the Second Lesson
Generative AI (GenAI) is having a moment. In just the past few months, diffusion and large language models have revolutionized the field of machine learning. From creating realistic images to generating human-like text, not a month goes by where there... View article
How I Transformed My Data Science Team’s Productivity by Introducing DataOps & MLOps
Introduction As a recent engineering graduate within a small but ambitious data science team, I was determined to increase our productivity and dive into the world of cloud technologies and DataOps/MLOps practices. Eager to learn and grow, I saw this... View article
Empowering Language Model Applications: Understanding and Evaluating Vector Databases in Production
Language models are powerful artificial intelligence algorithms that have the ability to generate human-like text based on the input they receive. They are general-purpose neural networks pre-trained on vast amounts of textual data and learn the statistical patterns and relationships... View article
Concepts for Reliability of LLMs in Production
Traditional NLP models are trainable, deterministic, and for some of them, explainable. When we encounter an erroneous prediction that affects downstream tasks, we can trace it back to the model, rerun the inference step, and reproduce the same result. We... View article