MLOps and LLMOps Crash Course—Part 5.| Daily Dose of Data Science
Struggling to get your GenAI prototype into production? Discover how LLMOps helps streamline deployment – fast, scalable, and reliable.| AI Accelerator Institute
MLOps and LLMOps Crash Course—Part 4.| Daily Dose of Data Science
MLOps and LLMOps Crash Course—Part 3.| Daily Dose of Data Science
MLOps and LLMOps Crash Course—Part 2.| Daily Dose of Data Science
MLOps and LLMOps Crash Course—Part 1.| Daily Dose of Data Science
LLMOps is emerging as a critical enabler for organizations deploying large language models at scale.| AI Accelerator Institute
I came across this quote in a happy coincidence after attending the second session of the evals course: It’s obviously a bit abstract, but I thought it was a nice oblique reflection on the topic being discussed. Both the main session and the office hours were mostly focused on the first part of the analyse-measure-improve loop that was introduced earlier in the week. Focus on the ‘analyse’ part of the LLM application improvement loop It was a very practical session in which we even took...| Alex Strick van Linschoten
Key insights from the first session of the Hamel/Shreya AI Evals course, focusing on a 'three gulfs' mental model (specification, generalisation, and comprehension) for LLM application development…| mlops.systems
Understanding the varied landscape of LLMOps is essential for harnessing the full potential of large language models in today's digital world.| AI Accelerator Institute
I finished the first unit of the Hugging Face Agents course, at least the reading part. I still want to play around with the code a bit more, since I imagine we’ll be doing that more going forward. In the meanwhile I wanted to write up some reflections on the course materials from unit one, in no particular order… Code agents’ prominence The course materials and smolagents in general places special emphasis on code agents, citing multipleresearchpapers and they seem to make some solid a...| Alex Strick van Linschoten
Chapter 10 of Chip Huyen’s “AI Engineering,” focuses on two fundamental aspects: architectural patterns in AI engineering and methods for gathering and using user feedback. The chapter presents a progressive architectural framework that evolves from simple API calls to complex agent-based systems, while also diving deep into the crucial aspect of user feedback collection and analysis. 1. Progressive Architecture Patterns The evolution of AI engineering architecture typically follows a p...| Alex Strick van Linschoten
Effectively deploying, managing & optimizing these models requires a robust set of tools and practices. Enter one of enterpise's most vital functions in 2025, LLMOps: a set of methodologies and tech stacks that aim to streamline the entire lifecycle of LLMs.| AI Accelerator Institute
TruEra recently announced the launch of a major update to its AI Observability offering. This launch dramatically expands TruEra’s LLM Observability capabilities, so that it provides value from the individual developer all the way to the largest enterprises, across the complete LLM app lifecycle. A developer can get started for free with the TruLens LLM […] The post LLM App Success Requires LLM Evaluations and LLM Observability appeared first on TruEra.| TruEra