Video: Managing Multiple ML Models For Multiple Clients (Hebrew)

For most ML-based SaaS companies, the need to fulfill each customer’s KPI will usually be addressed by matching a dedicated model. Along with the benefits of optimizing the model’s performance, a model per customer solution carries a heavy production complexity with it. In this manner, incorporating up-to-date data as well as new features and capabilities as part of a model’s retraining process can become a major production bottleneck.

In this talk, Ori discussed how Riskified scaled up modeling operations based on MLOps ideas and shared tools for how to set up your own continuous training ML pipeline.

Ori is an ML Engineer, part of the team responsible for building and operating Riskified’s MLOps tools, including the analytical and technical sides of maintaining production models and their (re)training process validation. He has five years of experience in multiple SW and ML engineering roles. He is passionate about SW, ML, and incorporating one into the other.