🫣 Is Data Science a dying job? with Almog Baku

In this episode, I had the pleasure of speaking with Almog Baku, a serial entrepreneur, consultant in Cloud, AI Infrastructure and Foundational models. We talk about Kubernetes, Large Language Models (LLMs), how to get them into production, and how data is becoming a more central piece of the ML landscape. We also Discuss Almog's newest project, Raptor ML, which helps ML teams productionize ML pipelines.

---
Timestamps:
0:00 Guest introduction
1:16 Almog's first ML experience
3:41 What led Dean to build DagsHub, and why DagsHub built integrations with many open-source tools.
5:13 Is the fear of Kubernetes in the data space justified?
6:31 Almog's predictions for the ML and MLOps world
10:44 Thoughts on ChatGPT and connecting with existing platforms and processes
15:21 If you build a model, and no one ever uses it, does it matter?
17:29 The future of data science, applied DS/ML, and how the industry will look like?
20:23 Moving from qualitative to quantitative ML engineering in the world of LLMs
28:09 Cutting edge techniques in LLMs – where the state of the art is, and discussion of AutoGPT
32:15 Shifts in MLOps resulting from the shift to LLMs
34:22 Almog's thoughts on the productionization of LLMs – how do you get LLMs in production
41:43 What is Raptor ML and how does it help ML team productionize their work?
45:03 What's the difference between Raptor ML and other solutions for deployment and containerizing ML models?
51:13 Counterintuitive thoughts on machine learning and MLOps
52:46 Recommendations for the audience

Relevant Links:

Recommendation Links:

Social Links: