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💼 AI in the Enterprise with Jeremie Dreyfuss
In this episode, Dean speaks with Jeremie Dreyfuss, Head of AI Research and Development at Intel, about the evolving role of AI in the enterprise. Jeremie shares insights into scaling machine learning solutions, the challenges of building AI infrastructure, and the future of AI-driven innovation in large organizations. Learn how enterprises are leveraging AI for efficiency, the latest advancements in AI research, and the strategies for staying competitive in a rapidly changing landscape.
Join our Discord community: https://discord.gg/tEYvqxwhah
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Timestamps:
00:00 Introduction and Overview
00:55 Challenges of Data Collection and Infrastructure
05:00 Optimizing Test Recommendations
14:42 Tips for Deploying Entire ML Pipelines
21:19 The Impact of Large Language Models (LLMs)
25:30 How to Decide About LLM Investment in the Enterprise
29:29 Evaluating Models and Using Synthetic Data
35:34 Choosing the Right Tools for ML and LLM Projects
45:21 The Beauty of Small Data in Machine Learning
48:22 Recommendations for the Audience
➡️ Jeremie Dreyfuss on LinkedIn – https://www.linkedin.com/in/jeremie-dreyfuss/
🌐 Check Out Our Website! https://dagshub.com
Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://x.com/TheRealDAGsHub
➡️ Dean Pleban: https://x.com/DeanPlbn
View all episodes
By Dean Pleban @ DagsHubIn this episode, Dean speaks with Jeremie Dreyfuss, Head of AI Research and Development at Intel, about the evolving role of AI in the enterprise. Jeremie shares insights into scaling machine learning solutions, the challenges of building AI infrastructure, and the future of AI-driven innovation in large organizations. Learn how enterprises are leveraging AI for efficiency, the latest advancements in AI research, and the strategies for staying competitive in a rapidly changing landscape.
Join our Discord community: https://discord.gg/tEYvqxwhah
---
Timestamps:
00:00 Introduction and Overview
00:55 Challenges of Data Collection and Infrastructure
05:00 Optimizing Test Recommendations
14:42 Tips for Deploying Entire ML Pipelines
21:19 The Impact of Large Language Models (LLMs)
25:30 How to Decide About LLM Investment in the Enterprise
29:29 Evaluating Models and Using Synthetic Data
35:34 Choosing the Right Tools for ML and LLM Projects
45:21 The Beauty of Small Data in Machine Learning
48:22 Recommendations for the Audience
➡️ Jeremie Dreyfuss on LinkedIn – https://www.linkedin.com/in/jeremie-dreyfuss/
🌐 Check Out Our Website! https://dagshub.com
Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://x.com/TheRealDAGsHub
➡️ Dean Pleban: https://x.com/DeanPlbn