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Blogs

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Exploring Retrieval Augmented Generation (RAG): enhancing LLMs with external knowledge

Harsh Mishra

SRE @One2N

Explore how Retrieval Augmented Generation (RAG) can enhance Large Language Models (LLMs) like ChatGPT by leveraging external knowledge sources. Understand the fundamentals of LLMs, the limitations of standalone models, and the key components of a RAG-based application.

October 8, 2024 | 9 min read

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How we used Fly.io to improve a Pipecat-AI chatbot

Ajinkya Wadekar

SRE @One2N

Learn how we improved our AI chatbot application using innovative solutions. Find out about the challenges we faced and the strategies we used to boost performance and scalability.

August 19, 2024 | 5 min read

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Understanding MLOps from a software engineers' perspective

Harsh Mishra

SRE @One2N

Spandan Ghosh

Content @One2N

Chinmay Naik

CEO @One2N

Jaideep Khandelwal

CTO @One2N

A comprehensive overview of MLOps for software engineers, covering key concepts, challenges, and best practices. Learn about knowledge silos, the MLOps lifecycle, automation, versioning, experiment tracking, and more.

July 30, 2024 | 7 min read

Talks

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A SRE’s Guide to LLMOps: Deploying and Managing AI⧸ML Workloads using Kubernetes

Harsh Mishra

SRE @One2N

In this webinar, Harsh Mishra, an SRE at One2N Consulting, discusses the unique challenges and solutions of LLMOps from an SRE viewpoint. The best practices he outlines will provide viewers with a great jumping off point for LLMOps, as well as future-proofing their cloud strategy to include the rapidly evolving field of GenAI and LLMs.

March 11, 2025 | 32 min

Watch Talk

ABC of LLMOps - What does it take to run self-hosted LLMs｜Rootconf mini 2024

Jaideep Khandelwal

CTO @One2N

Lessons from deploying open-source models on Kubernetes GPUs, managing vector DBs & building RAG apps

February 5, 2025 | 31 min

Watch Talk

From DevOps to MLOps - Scaling ML models to 2 million API requests per day

Chinmay Naik

CEO @One2N

Learn how to deploy and scale Machine Learning models to 2 Million+ requests/day using MLOps best practices. In this talk, you’ll learn how to go from data preparation to deployment to scaling ML models that can run at large scale - all without breaking the bank.

June 6, 2024 | 34 min

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