7 Steps to Level Up Your MLOps (and LLMOps) Maturity - Maria Vechtomova - Marvelous MLOps

🔥 Why do most organizations struggle to manage machine learning models and AI agents at scale, despite successful training?
🔥 What are the seven practical steps to significantly improve MLOps and LLMOps maturity, from version control to controlled deployments?
🔥 How can teams build reliable, reproducible, and observable ML systems to accelerate development and deliver consistent value?

Maria Vechtomova, MLOps Tech Lead and author at Marvelous MLOps, guides organizations through seven practical steps to enhance their MLOps and LLMOps maturity. With 12 years in data and 9 in MLOps, Maria emphasizes that scaling machine learning models and AI agents efficiently and reliably requires building robust, reproducible, and observable systems, moving beyond just training models to managing them at scale.

This presentation highlights that while many teams can train models, most struggle with large-scale management. Maria reveals that only 32% of companies properly implement CI/CD for continuous deployment, and a staggering number lack essential monitoring for their ML systems. She provides concrete advice and examples, including how to leverage platforms like Databricks, to accelerate development without risking system failures.

Did you know that Maria Vechtomova believes almost everyone deploying ML models and AI agents is "ignoring all the hard parts" of MLOps, leading to widespread immaturity in the field?



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🎬 CHAPITRES
00:00:00 Introduction: MLOps/LLMOps Maturity Challenges
00:01:20 Defining Production and Agentic Use Cases
00:05:14 Step 1: Version Control Everything and Connect the Dots
00:09:25 Step 2: Trace the Agent Logic with MLflow Tracing
00:11:22 Step 3: Implement Human-in-the-Loop Evaluation
00:13:37 Step 4: Introduce Comprehensive Monitoring
00:16:40 Step 5: Master Controlled Deployment
00:19:04 Step 6: Leverage Documentation as Competitive Advantage
00:20:00 Conclusion: Deploying ML/AI Agents by Addressing Hard Parts

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TITRES ET HASHTAGS ===
7 Steps to MLOps Maturity: Scaling AI Agents & ML Models with Confidence Hashtags : #MLOps #LLMOps #AIStrategy #DataBricks #MLFlow
Level Up Your AI: Practical Guide to Reliable & Reproducible ML Systems Hashtags : #MachineLearning #AIOps #DataScience #TechLeadership #MLDeployment
Beyond Training: Building Robust MLOps Pipelines for Enterprise AI Success Hashtags : #AIDevelopment #DataEngineering #CI_CD #AI_Governance #MLSystems