In this webinar
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 compounding 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 alignment to ensure long-term robustness of these heterogeneous end-to-end platform
Speakers
Alejandro Saucedo
Machine Learning Engineering Director, Seldon 