Building a Data-Centric End-to-End MLOps Workflow
About this webinar
Snorkel AI and Seldon have partnered to help data science teams build an end-to-end MLOps workflow that is scalable, auditable, and adaptable. Join our co-hosted session for a comprehensive guide to some of the biggest challenges in MLOps.
Building a machine learning (ML) pipeline can be challenging and time-consuming. Valuable data is often scattered across an organization and locked in documents, contracts, patient files, and other formats. To train ML models, this data must be curated and labeled. Serving ML models can be difficult and expensive, especially at scale and when experiencing spikes in demand. And – inevitably – concept and data drift over time cause degradation in a model’s performance.
Snorkel AI and Seldon have partnered to help data science teams build an end-to-end MLOps workflow that is scalable, auditable, and adaptable.
What you'll learn
- How to accelerate training dataset creation with programmatic labelling
- Best practices for ML models deployment including monitoring, logging, and more
- Strategies to automate the ML iteration cycle to accommodate data & concept drift