Scaling Machine Learning Models from the laptop to production is non-linear. Different rules and laws apply at scale. Ketan Umare (CEO Union.ai, TSC Flyte.org), will cover the three laws for ML at scale. These laws will be grounded using anecdotes that the team encountered while working with some of the top companies utilizing ML in production. The talk will cover the following topics,
– Machine Learning products are resource intensive. Oftentimes the cost of ML projects balloon and access to infrastructure is scarce without predefined ROI.
– ML at scale is a team sport. Production teams need a common platform to collaborate efficiently. Small in-efficiencies in collaboration can slow down an entire organization.
If things can go wrong, at scale they will go wrong. We need a safety net – versioning, reproducibility are important in delivering robust ML products at Scale. To deliver these robust products, just code and data is not enough, it is essential to capture the infrastructure & configuration.