In this in-depth technical session, be guided through advanced deployment strategies using Seldon Core serving on Kubernetes. The webinar covers everything from how Seldon fits into the MLOps lifecycle, to the inner workings of Seldon Core V2, including multi-model serving, overcommit capabilities, and pipeline orchestration. A live demo showcases how quickly and flexibly users can deploy, manage, and experiment with ML models using the platform, with insights for both newcomers and seasoned MLOps professionals.
About
Learnings
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Where Seldon fits in the MLOps lifecycle: Understand how Seldon focuses on the last mile of machine learning – serving and monitoring models in production – with flexibility across tools, languages, and infrastructure.
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Key differences between Core V1 and Core V2: Learn how Seldon Core 2 improves on 1 with data-centric pipelines, multi-model serving, and simplified infrastructure management.
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How multi-model serving and overcommit work: See how Core 2 enables greater scalability and resource efficiency by loading multiple models onto shared inference servers and managing memory intelligently.
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Deploying models, pipelines, and experiments with ease: Follow along with a live demo showing how to create and manage models, build pipelines, and run A/B tests using simple YAML configurations and the CLI.
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Extending with custom runtimes and pre-built integrations: Explore how Seldon supports custom logic, open inference protocols, and common ML libraries like TensorFlow, PyTorch, and scikit-learn, as well as custom preprocessing and post-processing logic.
Seldon
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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