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.

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

In this webinar, we will talk about enterprise scale, fully automated MLOps solutions for Trade Promotions, looking into the impact this can create for CPG companies, through a real-world example.

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.

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.