Data-Centricity Explained: How to Keep Data at the Core of Your MLOps

About

Join Seldon’s SVP of Product Tanu Chellam to explore why traditional, model-centric approaches are no longer enough for modern machine learning systems, and how a data-centric mindset can transform your ability to deploy, scale, and monitor ML applications.

Learn how Seldon Core 2 was built to prioritize data flows over individual models, and how this approach leads to more scalable, modular, and trustworthy real-time systems. Ideal for ML platform teams, data scientists, and MLOps engineers building production-grade AI.

Learnings
  • What is Data Centricity: Learn why shifting focus from models to data flows improves ML system design.

  • Why It Matters: Address real-world ML challenges like complexity, scale, and regulatory risk.

  • Inference Graphs: See how chaining modular components improves flexibility and reuse.

  • Core Benefits: Lower retraining costs, faster debugging, better monitoring, and auditability.

  • Built for Real-Time ML: Core v2 supports real-time, multi-model, modular deployments via Kafka and Kubernetes.

  • Choosing MLOps Tools: Get a checklist for evaluating flexibility, standardization, and observability.

  • What’s Next: Preview the upcoming deep dive into Seldon Core v2 and real-time pipelines.

Tanu is a seasoned tech entrepreneur with 17 years of experience managing people and products across diverse industries and global locations. She is currently the VP Product at Seldon and also serves as Chair of the Board at Atlas Rosetta, a tech venture consultancy. With a product and technology-oriented focus, Tanu has held operational, strategic, board, and advisory roles in fields such as cybersecurity, e-commerce, fintech, social impact, design tech, and machine learning. Her achievements include multiple awards for outstanding leadership in managing people and products in both corporate and startup environments.

Complex, real-time use cases is what we do best

Talk with a expert to explore how Seldon can support more streamlined deployments for real-time, complex projects like fraud detection, personalization, and so much more.

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