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October 24-28
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Friday, October 28 • 11:00am - 11:35am
MetaOps: Metadata Operations For End-To-End Data & Machine Learning Platforms - Alejandro Saucedo, The Institute for Ethical AI & Machine Learning

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Organisations have been growingly adopting and integrating a non-trivial number of different frameworks at each stage of their machine learning lifecycle. Although this has helped reduce time-to-value for real-world AI use-cases, it has come at a cost of complexity and interoperability bottlenecks. Each stage in the end-to-end lifecycle involves different stakeholders that make decisions and perform actions that can modify data and/or ML components with use-case-specific but ever compoinding risks, resulting in a growing need to ensure a minimum-level of metadata is collected, tracked and managed. This becomes growingly important due to the need to ensure relevant overarching compliance requirements, as well as architectural requirements on lineage, auditability, accountability and reproducibility. In this session we will dive into the challenges present in the metadata layer of large-scale systems, as well as tooling, best practices and solutions that can be adopted to tackle these challenges. We will discuss the rise of the metadata management systems, the challenges they have been able solve, as well as critical shortcomings where ecosystem-wide collaboration will be key from tooling-level alignemnt to ensure long-term robustness of these heterogeneous end-to-end platform.

avatar for Alejandro Saucedo

Alejandro Saucedo

Engineering Director, Seldon Technologies
Alejandro Saucedo is the Director of Machine Learning Engineering at Seldon Technologies, where he leads teams of machine learning engineers focused on the scalability and extensibility of machine learning deployment and monitoring products with over 5 million installations. Alejandro... Read More →

Friday October 28, 2022 11:00am - 11:35am EDT
251 ABC
  Machine Learning + Data