MLOps Live Webinar Series

Session #22

How Seagate Runs Advanced Manufacturing at Scale

In this MLOps Live webinar session, Seagate shared their story of how they successfully tackled their challenges to handle continuous data engineering at scale while keeping costs low and productivity high.

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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 #20

How to Easily Deploy Your Hugging Face Model to Production at Scale

Watch Julien Simon (Hugging Face), Noah Gift (MLOps Expert) and Aaron Haviv (Iguazio) discuss how you can deploy models into real business environments, serve them continuously at scale, manage their lifecycle in production, and much more in this on-demand webinar!

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Session #19

From AutoML to AutoMLOps: Automated Logging & Tracking of ML

In this session, we outline the challenges, describe open-source tools available for Auto-MLOps, and finish off with a live demo.

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Session #18

Best Practices for Succeeding with MLOps

In this webinar Noah Gift and Yaron Haviv discuss the latest trends and tried-and-true methods for getting data science to production.

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Session #17

Scaling NLP Pipelines at S&P Global (IHS Markit)

In this webinar we discuss how S&P Global (IHS Markit) built a sophisticated and automated NLP pipeline that works at scale, making massive amounts of PDF documents searchable and indexable.

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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 #15

Automated Model Management for CPG Trade Effectiveness

Learn how MLOps practices help Trade Promotion Managers optimize trade spends with high precision

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Session #14

Automating & Governing AI Over Production Data on Azure

Explore how to facilitate end-to-end automation of the production pipeline and how to govern AI in an automated way.

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Session #13

How Feature Stores Accelerate & Simplify Deployment of AI to Production

Discover how feature stores can be used to accelerate AI deployment, show a demo and present a customer use case.

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Session #12

Simplifying Deployment of ML in Federated Cloud and Edge Environments

In this technical training session, we explore how to run a distributed cloud or edge application on Amazon Cloud and Outposts with the Iguazio MLOps Platform.

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Session #11

Handling Large Datasets in Data Preparation & ML Training Using MLOps

In this technical training session, we learn how to use Dask, Kubernetes, and MLRun to scale data prep and ML training with ease.

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Session #10

Siemens on the Importance of Data Storytelling in Shaping a Data Science Product

Siemens AG's Data Analytics Solutions Expert, Vijay Pravin Maharajan, gives a deep dive into storytelling with data and how to make sure all your hard work on developing the right model and building the full-blown ML pipeline also result in a visually pleasing and simple-to-read visual dashboard for quick internal and external adoption.

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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 #8

NetApp's Michael Oglesby on Building ML Pipelines Over Federated Data & Compute Environments

Yaron Haviv speaks with Netapp’s Senior Technical Director AI & Data Engineering about constructing ML pipelines across federated data and compute environments, and building production-ready AI applications that work in hybrid environments (on-prem, cloud and edge).

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Session #7

Product Madness (an Aristocrat co.) on Predicting 1st-Day Churn in Real Time

Product Madness' Head of Data Science discusses how technology and new work processes help the gaming and mobile app industries predict and mitigate 1st-day (or D0) user churn in real time, and explore feature engineering improvements to the RFM (Recency, Frequency, and Monetary) churn prediction framework: The Discrete Wavelet Transform (DWT).

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Session #6

Greg Hayes on Breaking the Silos Between Data Scientists, Engineers and DevOps with New MLOps Practices

Ecolab’s Data Science Director discusses how his company is accelerating the deployment of AI applications by using new MLOps methodologies, leveraging microservices and building easier processes for the teams to create a culture of collaboration.

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Session #5

Netapp’s Shankar Pasupathy on How to Build a Predictive Maintenance and Actionable Intelligence Solution at Scale

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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Session #3

How to Deal With Concept Drift in Production with MLOps Automation

Iguazio’s Data Scientist discusses how to detect and handle problems that arise when models lose their accuracy and how to implement concept drift detection and remediation in production. He shows how to automate MLOps processes at scale, to handle drift detection using open source tools.

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Session #2

Quadient’s Jason Evans on Saving Time & 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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Session #1

S&P Global’s Ganesh Nagarathnam on Bringing ML Pipelines to Production

In the very first episode of the series, Yaron Haviv speaks with S&P Global’s Director of Analytics & ML Engineering about how industry experts are operationalizing ML, how to map a business problem into an automated ML production pipeline, and how to run AI in production at scale.

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MLOps for LLMs

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