In this episode of AI Infra Stories, we had the pleasure of hosting Elad Cohen(Vice President of Core Research at Pagaya). Elad shared with us a common problem that many companies face - research chaos. He delved into the challenges of productizing models in their environment and how they overcame this obstacle through standardization and cookbooks. These approaches allowed for the unification of work and enabled automation and growth. The episode provided valuable insights into streamlining research processes and optimizing productivity.

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Join our Discord community - ⁠https://discord.gg/JcNRrJ5nqW⁠

Twitter -⁠ https://twitter.com/ai_infra_live

⁠Linkedin -⁠ https://linkedin.com/company/ai-infra-stories/⁠

Almog - ⁠https://www.linkedin.com/in/almogbaku/⁠

Amit -⁠ https://www.linkedin.com/in/amitbendor/⁠

Elad - https://www.linkedin.com/in/elad-cohen-ai/

Feature Stores are a new and exciting innovation in AI infrastructure. They help organizations store, manage and serve their machine learning features to support the model serving.

In this episode of AI Infra Stories, we're thrilled to bring you a conversation with Hien Luu and Brian Seo from Doordash. They'll take us through the story of how they built their Feature Store and why it was crucial to their operations. You'll hear about their challenges while scaling it up and how they solved them. This is a rare opportunity to gain insight into one of the cutting-edge AI infrastructure projects in the industry and learn from the experts themselves. Tune in for a fascinating episode of AI Infra Stories!

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Doordash's feature store blog post - https://doordash.engineering/2020/11/19/building-a-gigascale-ml-feature-store-with-redis/

Join our Discord community - https://discord.gg/JcNRrJ5nqW

Almog - https://www.linkedin.com/in/almogbaku/

There's nothing better than pre-trained, open-source models! You can use them to solve a variety of common problems, such as image classification, speech-to-text, or even text generation.

But the problem is - they come in different formats and shapes. From native PyTorch and Tensorflow, Hugging face, or Torch lightning, it can take quite some time to set those up for inference, finetuning, or evaluation.

In this episode, we hosted Patrick Barker from One Medical, who shared his unique solution to the problem, how they shaped it, and how this is all connected to Kubernetes.

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Join our Discord community - https://discord.gg/JcNRrJ5nqW

Almog - https://www.linkedin.com/in/almogbaku/

Deploying a model is not the end of the story. Most of the time, it's only the beginning of a new one. Model tracking, data drift, inference performance, and many more challenges are hiding in the journey of rolling a model for users.

In this episode, we hosted Simarpal Khira from Intuit, who shared with us Intuit's story of building their internal Monitoring Platform with us. We learned about their challenge, why they decided to build something, and how people use it internally.

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Join our Discord community - https://discord.gg/JcNRrJ5nqW

Simarpal Linkedin - https://www.linkedin.com/in/simarpal-khaira-6318959/

Discord community - https://discord.gg/JcNRrJ5nqW

Twitter - https://twitter.com/ai_infra_live

Linkedin - https://linkedin.com/company/ai-infra-stories/

Almog - https://www.linkedin.com/in/almogbaku/

Amit - https://www.linkedin.com/in/amitbendor/