MLOps @ 127.0.0.1 2024
2024-05-21
-- online --
who it's for
mlops engineers, machine learning engineers, platform engineers, site reliability engineers, data engineers, devops engineers, compliance and regulatory engineers
topics
continuous monitoring pipeline for LLMs; golden dataset offline validation; embedding-space clustering for drift; retrieval-augmented generation (RAG); vector databases for embeddings; model registry and drift monitoring; CI/CD for ML and APIs; hybrid cloud with Anthos and OpenStack; edge inference on Kubernetes over 5G; YOLO fine-tuning on field images; embeddings versioning and prompt lifecycle; RAG+vector DB chat with RLHF feedback
| title | author | length |
|---|---|---|
| Controlling Dependency on Third‑Party Large Language Models | Marco Rossi | 41m |
A practical plan for SMEs to reduce reliance on third‑party LLMs by building a continuous monitoring pipeline that detected a real performance drop when a… | ||
| Compose GenAI Apps: May the Force Be with Your Data and Models | Michel Murabito | 33m |
Explains how composable GenAI architectures plus Retrieval-Augmented Generation (RAG) can reduce development time from months to hours by using a concrete… | ||
| MLOps for a hybrid AI platform compliant with European law | A. Chiarini | 50m |
Describes a hybrid AI platform deployed on OpenStack with Kubernetes containers and extended to Google Cloud using Anthos, running edge inference over 5G… | ||
| Across the Pond | Michele Zanchi, Domenico Messina | 42m |
Tenaris deployed a computer-vision safety gate using YOLO fine-tuned on ~2,000 field images, served as a Python REST container on Kubernetes with ~300 ms CPU… | ||
| MLOps vs LLMOps | Alessandra Bilardi | 40m |
Compares traditional MLOps pipelines (code, model registry, baseline datasets, and embedding lifecycle) with LLM-first architectures that use vendor… | ||
| nothing matches | ||