Tag Archive: LLMs

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

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

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

Fixing the MLOps Survey on LLMs with ChatGPT API: Lessons Learned

Large Language Model (LLM) is such an existing topic. Since the release of ChatGPT, we saw a surge of innovation ranging from education mentorship to finance advisory. Each week is a new opportunity for addressing new kinds of problems, increasing human productivity, or improving existing solutions. Yet, we may wonder... View article

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