CASE STUDY

Ecolab Accelerates AI Deployment from 12 Months to 30-90 Days with Iguazio & Microsoft

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#MLOpsLive

MLOps Live Webinar Series

This is not another high-level webinar series about AI. These sessions go beyond theory, with industry leaders sharing practical advice and demonstrating how they’ve made real business impact by bringing data science to life.

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Upcoming MLOps Live Session

Expert Panel
AI for Connected Vehicles

The promise of connected vehicles has captured the minds of many but the reality is that the world of connected vehicles is complex to set up and difficult to manage.

Join our panel and hear about how to utilize and process data in the best way, using the right AI infrastructure and MLOps software to support the most daring and complex use cases for connected vehicles.
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Featured Sessions

Session #13

How Feature Stores Accelerate & Simplify Deployment of AI to Production
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Session #3

How to Deal With Concept Drift in Production with MLOps Automation
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Session #7

Product Madness (an Aristocrat co.) on Predicting 1st-Day Churn in Real Time
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Get to the Core of

MLOPS &
AUTOMATION

EXPERIMENT &
DATA TRACKING

ONLINE & OFFLINE
FEATURE STORES

PERFORMANCE
AND SCALE

KUBEFLOW AND
OPEN SOURCE

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Session #9

NVIDIA on Industrializing Enterprise AI with the Right Platform

Tony Paikeday, Senior Director of AI Systems of NVIDIA, discusses 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 or DevOps-grounded approach.
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Session #4

Microsoft & GitHub on Git-Based CI / CD for Machine Learning & MLOps

David Aronchick, Head of OSS ML Strategy at Microsoft, Marvin Buss, Azure Customer Engineer at Microsoft, and Zander Matheson, Senior Data Scientist at Github discuss using Git to enable continuous delivery of machine learning to production, enable controlled collaboration across ML teams, and solve rigorous MLOps needs.
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Session #2

Quadient’s Jason Evans on Saving Costs Bringing AI to Production

Quadient’s Director of DXP Innovation shares how industry leaders save costs and get to market faster by streamlining the operationalization of ML, how to run Al models in production at scale without scaling your costs, and how to reduce time and resource consumption by leveraging ML pipeline automation and open source technology.
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