Demo

Check out our new Telco demo showcasing a real-time GenAI agent co-pilot built on top of Iguazio

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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.

Previous Webinar Call for speakers

Previous Webinar Session

How to Manage Thousands of Real-Time Models in Production

This is a very special session of MLOps Live. Two years after we first hosted Seagate on our webinar series, they returned to share the progress they have made with their ML pipeline and MLOps automation capabilities, and how they're managing thousands of AI models in production today.

Watch to discover Seagate's secrets to successful deployment, orchestration and management of AI applications in real-time at scale.
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Get to the Core of

MLOPS &
AUTOMATION

GEN AI
APP BUILDING

MULTI CLOUD &
ON-PREM DEPLOYMENTS

PERFORMANCE
AND SCALE

RISK
MITIGATION

Watch On Demand

Session #28

Improving LLM Accuracy & Performance

This MLOps Live session shares best practices and pragmatic advice on successfully improving the accuracy and performance of LLMs while mitigating challenges like risks and escalating costs.
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Session #27

LLM Validation & Evaluation

Learn firsthand how to build an automated flow to accelerate, evaluate and fine tune an LLM effectively with Ehud Barnea (Tasq.ai), Yaron Haviv and Guy Lecker (Iguazio).
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Session #25

GenAI for Financial Services

In this MLOps Live Webinar session, Larry Lerner Partner & Global Lead - Banking and Securities Analytics at McKinsey & Company joined Yaron Haviv to discuss how leading global Financial Service companies are using GenAI today to serve their clients better, improve operations, and get ahead of the pack.
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Session #24

How to Build an Automated AI ChatBot

In this MLOps Live webinar session, Sense shares how they built an AI chatbot, overcoming complex challenges such as building a complex natural language processing ( NLP) serving pipeline with custom model ensembles, to track question-to-question context and enable sentiment tracking.
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Session #21

HCI’s Journey to MLOps Efficiency

In this session, Yaron and Jiri will be sharing enterprise secrets to establishing efficient systems for ML/AI - including building ML pipelines, leveraging a feature store for ML feature sharing and reuse, and automating the entire data science process to take repetitive manual work out of the equation.
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Session #16

Building a Real-Time ML Pipeline with a Feature Store

In this webinar, we discuss the challenges associated with online feature engineering across training and serving environments, how feature stores enable teams to collaborate on building, sharing and managing online and offline features across the organization.
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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 #5

NetApp’s Shankar Pasupathy on Building Scalable Predictive Maintenance

The Sr. Director of Active IQ AI and Data Engineering explains how NetApp migrated from Hadoop to a microservices, Kubernetes, serverless environment, and built a solution for predictive maintenance and actionable intelligence that responds in real time to 10 trillion data points collected from storage controllers globally per month.
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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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