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Latest Videos

Automating and Governing AI over Production Data on Azure - MLOPs Live #14 w/Microsoft

Many enterprises today face numerous challenges around handling data for AI/ML. They find themselves having to manually extract datasets from a variety of sources, which wastes time and resources. In this session, we discuss end-to-end automation of the production pipeline and how to govern AI in an automated way. We touch upon setting up a feedback loop, generating explainable AI and doing all of this — at scale.

Industrializing Enterprise AI with the Right Platform - MLOps Live #9 - With NVIDIA

We discuss how enterprises need a platform that brings together tools to streamline data science workflow with leading edge infrastructure that can tackle the most complex ML models — one that can bring innovative concepts into production sooner, integrated within your existing IT/DevOps-grounded approach.

Simplifying Deployment of ML in Federated Cloud and Edge Environments - MLOPs Live #12 - with AWS

We discuss some common applications for machine learning at the edge and the main challenges associated with deploying distributed cloud and edge applications. We then wrap up the session with a live demo showing how to run a distributed cloud or edge application on Amazon Cloud and Outposts with the Iguazio Data Science Platform.

How Feature Stores Accelerate & Simplify Deployment of AI to Production MLOPs Live #13

The breakdown:

00:00 - Intro
02:15 - MLOps Overview
05:03 - Feature Engineering
07:44 - MLOps Workflow
10:44 - Solution: Feature Store
14:25 - Feature Store Competitive Landscape
17:03 - Features of a Feature Store
21:01 - CTO: Feature Store Sneakpeak
25:55 - Python Code example
27:57 - ML Pipeline example
30:07 - Covid-19 Patient Deterioration
33:26 - LIVE DEMO
52:45 - QA

Lessons Learned on Operationalizing Machine Learning at Scale with IHS Markit

According to Gartner, over 80% of data science projects never make it to production. This is the main problem that enterprises are facing today, when bringing data science into their organization or scaling existing projects. In this session, Senior Data Scientist Nick Brown will share his lessons learned from operationalizing machine learning at IHS Markit. He will discuss the functional requirements required to operationalize machine learning at scale, and what you need to focus on to ensure you have a reliable solution for developing and deploying AI.