Building a production-scale ML platform

Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.

Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products.

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.

Pamela Vagata explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Join in to learn how the deep learning model outperforms a feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance.

Aleksander Madry discusses major roadblocks that prevent current AI frameworks from having a broad impact and outlines approaches to addressing these issues and making AI frameworks truly human-ready.

Nanda Vijaydev explains how to spin up instant ML/DL environments using containersall while ensuring enterprise-grade security and performance. Find out how to provide your data science teams with on-demand access to the tools and data they need, whether on-premises or in the cloud.