WeAreDevelopers Logo WeAreDevelopers
Sign in
For You Scheduled soon LIVE
WeAreDevelopers LIVE • Nov 27, 2024

From Traction to Production: Maturing your LLMOps step by step

Maxim Salnikov

Struggling to move your generative AI from a fragile experiment to a reliable production feature? Master the step-by-step LLMOps framework designed specifically for application developers.

Pause
Mute Enter Fullscreen
#1 about 5 min

Business motivations and adoption challenges for generative AI

Early adoption of artificial intelligence faces roadblocks like expertise gaps, data integration, and complex evaluation.

#2 about 3 min

Defining LLMOps and its workflow automation benefits

Specialized operations for large language models focus on collaboration, reproducibility, and delivering continuous user value.

#3 about 4 min

Key differences between traditional MLOps and LLMOps

While MLOps relies on model accuracy and data environments, LLMOps centers on prompting, agents, and cost metrics.

#4 about 6 min

Building components of a real-world LLM lifecycle

Managing an AI project requires distinct loops for ideation, prompt development, structured deployment, and strict compliance governance.

#5 about 5 min

Navigating the four stages of LLMOps maturity

Organizations progress from manual API calls to fully optimized, systematic control points for versioning and continuous deployment.

#6 about 5 min

Centralizing LLMOps workflows within Azure AI Foundry

Microsoft's scalable enterprise toolchain provides infrastructure to automate, deploy, and govern cutting-edge foundation models securely.

#7 about 4 min

Selecting and benchmarking models in the catalog

Developers can compare thousands of open-source and proprietary models using specific metrics for latency, cost, and fluency.

#8 about 5 min

Orchestrating applications and RAG patterns with Prompt Flow

Developing code-first graphs allows systematic control over LLM routing, chunking layers, credential management, and prompt variation.

#9 about 2 min

Fine-tuning enterprise language models for niche applications

Incorporating proprietary company data directly into model weights provides reliable completions for highly specific operational requirements.

#10 about 5 min

Deploying outputs and maintaining content safety protocols

Fully managed endpoints support adjustable content filters, latency tracking, and autoscaling capabilities to ensure reliable application performance.

Matching moments

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · World Congress 2023

4:19 min

Introduction to DevOps for AI and MLOps

Aarno Aukia · LIVE

2:14 min

Differences between traditional MLOps and GenAIOps

Maxim Salnikov Maxim Salnikov · World Congress 2025

2:03 min

Integrating infrastructure enablers for LLMOps operations ecosystems

Anshul Jindal Anshul Jindal · World Congress 2025

3:56 min

Solving application deployment complexities using LLMOps pipelines

Anshul Jindal Anshul Jindal · World Congress 2025

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

More from WeAreDevelopers LIVE

WeAreDevelopers LIVE - Playing in Production thumbnail
1:06:40
7 days ago

WeAreDevelopers LIVE - Playing in Production

Andrew MacLean, Daniel Cranney, Christian Heilmann

WeAreDevelopers LIVE - Is CSS Catching Up to Flash? thumbnail
1:03:33
21 days ago

WeAreDevelopers LIVE - Is CSS Catching Up to Flash?

Chris Heilmann, Daniel Cranney, Bramus Van Damme

WeAreDevelopers LIVE - Project Chopin: Make Your Team and Agents Play Along thumbnail
1:21:17
28 days ago

WeAreDevelopers LIVE - Project Chopin: Make Your Team and Agents Play Along

Chris Heilmann, Daniel Cranney, Krzysztof Cieślak

Fake or News - AI Toothbrushes, Carrier Pigeon Tech & Life on Mars - Krzysztof Cieślak thumbnail
4:23
29 days ago

Fake or News - AI Toothbrushes, Carrier Pigeon Tech & Life on Mars - Krzysztof Cieślak

Chris Heilmann, Daniel Cranney

WeAreDevelopers LIVE - Open Source Agent Toolkits thumbnail
1:00:38
about 1 month ago

WeAreDevelopers LIVE - Open Source Agent Toolkits

Chris Heilmann, Daniel Cranney, Eddie Jaoude

Fake or News - AI Music Detectors, Translating Ancient Languages & Tiny Heads in Teslas - Eddie Jaoude thumbnail
6:16
about 1 month ago

Fake or News - AI Music Detectors, Translating Ancient Languages & Tiny Heads in Teslas - Eddie Jaoude

Chris Heilmann, Daniel Cranney

Related videos

From Traction to Production: Maturing your GenAIOps step by step thumbnail
25:27
World Congress 2025 • about 1 year ago

From Traction to Production: Maturing your GenAIOps step by step

Maxim Salnikov

DevOps for AI: running LLMs in production with Kubernetes and KubeFlow thumbnail
34:21
LIVE • about 2 years ago

DevOps for AI: running LLMs in production with Kubernetes and KubeFlow

Aarno Aukia

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment thumbnail
25:41
World Congress 2024 • about 2 years ago

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

Joy

MLOps - What’s the deal behind it? thumbnail
29:24
World Congress 2022 • over 4 years ago

MLOps - What’s the deal behind it?

Nico Axtmann

Effective Machine Learning - Managing Complexity with MLOps thumbnail
45:45
World Congress 2021 • over 5 years ago

Effective Machine Learning - Managing Complexity with MLOps

Simon Stiebellehner

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices thumbnail
29:20
World Congress 2025 • about 1 year ago

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Anshul Jindal

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.