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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.

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#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

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Upcoming sessions on this topic

Open session

From Prompt Engineering to Harness Engineering: Building Observable and Governed GenAI Systems

  • Lakshmi Devi Prakash

    JPMorgan Chase

    VP, Applied AI/ML Lead

Open session

Prompt Debt: Why Your AI System Is Frozen in Time

  • Suriya Prakash B

    Red Hat

    Principal Software Engineer

Open session

Teaching AI Coding Agents to Build It Right the First Time

  • Sajeetharan Sinnathurai

    Microsoft

    Principal Product Manager

Open session

AI Is the Easy Part: Why Faster Engineers Don't Make Faster Businesses

  • Bharat Sharma

    Booking

    Senior Engineering Manager

Open session

Architecting Trusted AI: Coupling Multi-Agent Systems with dbt Semantic Store for Zero-Hallucination

  • Pushkar Mishra

    J.P. Morgan

    Senior Vice President

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    DigitalOCean

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