Building an ML Factory | Adi Hirschtein, Iguazio (May 2022)

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 organization
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.

[HADI-UPDATE]

If hard work becomes a habit, So success becomes a matter. Whoever is watching my comment at this time, we are all unknown to each other, yet I pray to God that if there is any tension going on in your life, then it should go away. And may you always be happy

[davidanalyst]

I desperately wanted to learn about ML or building a ML factory but all i got was a bunch of business-speak gibberish