Building an ML Factory: How Enterprises use MLOps Automation to Continuously Roll out New AI Services


MLOps has emerged as key focus area for Enterprise. But why? The answer is simple. To remain competitive in this era of digital transformation it’s become a business imperative to establish a competency around machine learning and deep learning application delivery. Now, enterprises are starting to take the next step in making the MLOps process repeatable, scalable and reproducible, so they can continuously infuse the business with innovation.

In this talk we will deep dive into 3 Enterprise case studies where leading organizations have built automated machine / deep learning pipelines, generating real business value from AI:
1. Serving real time recommendations for retail
2. Scaling NLP pipelines to make thousands of PDFs searchable and indexable for the
3. Deploying 40+ data products at a large airline group to tackle fraud, optimize flight
routes to reduce CO2 emissions and improve pilot training
We’ll cover the organizational and technological aspects to consider when building up your MLOps capabilities and practical tips for success.


Adi Hirschtein brings 20 years of experience as an executive, product manager and entrepreneur building and driving innovation in technology companies. As the VP of Product at Iguazio, the MLOps platform built for production and real-time use cases, he leads the product roadmap and strategy. His previous roles spanned technology companies such as Dell EMC, Zettapoint and InfraGate, in diverse positions including product management, business development, marketing, sales and execution, with a strong focus on machine learning, database and storage technology. When working with startups and corporates, Adi’s passion lies in taking a team’s ideas from their very first day, through a successful market penetration, all the way to an established business. Adi holds a B.A. in Business Administration and Information Technology from the College of Management Academic Studies.

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