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mlops world

OCT 25-26 Austin, TX

Exploring Machine Learning Tech Behind the Scenes

VIRTUAL upcoming sessions

Inside the ML Engineer's Toolbox:
MLOps & LLMOps Unveiled

Denys Linkov, ML Lead & Yoyo Yang, ML Engineer Voiceflow
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Indrani Gorti, Director, Data and ML Platform, Loblaw Digital

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Episode 3

MLOpsWorld End User Series!
Talk TBA soon!
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Why the 'Stacked up! series?

It’s important to note that there is no “one size fits all” solution or “perfect” stack for machine learning. The challenges faced by different teams can vary significantly, depending on factors such as budget, data availability, model availability, and other inhibitors specific to a given application area. This highlights the need for open conversations and knowledge sharing to help teams make informed decisions about their ML tech stacks.

The Stacked Up! series provides a valuable forum for discussing what works and what doesn’t, and how different teams have landed on their current ML tech stacks.

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FORMAT

Each session is held virtually and free to attend. it will be a 45 min interview style where we distill;
  1. Company & Industry context: What THE ML is Solving & how?
  2. Data
  3. what tools prevailed & why?
  4. Q&A
mlops world

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aNY QUESTIONS? lET US KNOW & wE'LL BE IN TOUCH!

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Podcast Title: Bytes and Baskets: Crafting Canada’s eCommerce Experience with MLOps Mastery

Abstract: Join us on “Bytes & Baskets” as we delve into the intricacies of building a machine learning platform for eCommerce experiences with some of Canada’s leading brands in online grocery shopping, beauty, pharmacy, and apparel. Uncover the challenges, triumphs, and innovations that shape the future of online retail through engaging discussions with Indrani Gorti.

The Stacked Up! series with Hien Luu, Head of ML Platform, DoorDash is a response to our MLOps World Committee’s concern about the lack of understanding and clarity around the tools, and ML stacks used across different ML teams, and application areas. As part of our mandate to give back to the ML community we’ve designed this series to be a non-commercial, open discussion around what tech companies use behind the scenes in order to help shed light for others working in the field.

There is no “one size fits all solution” or “perfect” stack. Often, core challenges centre around different factors; budget, data, models availability and a variety of other inhibitors specific to a given application area. The space can be ripe with options for solutions, but what works for some is completely unsuitable for others.

This is why conversations on the topic are important and helpful. What’s worked, what hasn’t, and how teams have landed on their current ML tech stack are important conversations that will help drive our ML community forward.

If this is something you’re interested in helping with – we look forward to hearing from you!