In this episode, Dean and Natanel Davidovits explore the intricacies of AI and machine learning, focusing on model efficiency, the use of APIs versus self-hosting, and the importance of defining success metrics in real-world applications. They discuss the challenges of data quality and labeling, the evolving role of data scientists in the age of LLMs, and the significance of effective communication between data science and product teams. The conversation also touches on the future of robotics in AI and the need for specialization in a rapidly changing landscape. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00 Introduction to Natanel Davidovits 02:10 Optimizing AI Models for Real-World Tasks 03:47 Success Metrics in Industry vs. Academia 07:52 The Importance of Communication Between Teams 11:33 Handling Data Quality and Labeling Challenges 12:11 The Impact of LLMs on Data Science Careers 16:29 Navigating Specialized Domain Data 22:15 Trends in Machine Learning and AI 27:27 The Future of AI and Robotics 28:28 The Role of AI in Physics 33:36 Controversial Views on AI and Machine Learning 34:05 Final Thoughts and Recommendations ➡️ Natanel Davidovits on LinkedIn – https://www.linkedin.com/in/natanel-davidovits-28695312/ 🌐 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

Jeremie Dreyfuss, Head of AI Research and Development at Intel, shares his expertise on scaling machine learning solutions and AI infrastructure. He delves into the challenges of data collection, emphasizing the need for robust systems in high-stakes environments. The discussion touches on deploying ML models and the transformative impact of large language models. Jeremie also explores strategies for effective AI development, highlighting the importance of balancing user needs with sophisticated algorithms. His insights illuminate the path forward for enterprises navigating the AI landscape.

In this episode, Dean speaks with Dror Haor, CTO at SeeTree, about the challenges of deploying AI in agriculture at scale. They explore how SeeTree integrates AI and sensor fusion to manage vast amounts of remote sensing data, helping farmers improve crop yields with high accuracy at low costs. Dror shares insights on handling data drift, customizing models for different regions, and balancing the trade-offs between cost and performance. This conversation dives deep into practical machine learning applications in agriculture, offering valuable lessons for anyone working with large-scale data and AI. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00 Introduction 00:32 Production in machine learning at SeeTree 07:34 Sensor fusion in machine learning 16:26 Balancing accuracy and cost in agriculture 20:09 Customizing models for different customers and crops 24:19 Dealing with data in different domains 30:10 Tools and processes for ML at SeeTree 35:58 Building for scale 40:17 Collecting user feedback and self-improving products 42:45 Exciting developments in ML & AI 45:12 Hot takes in ML - Overfitting is good 46:34 Recommendations for the Audience ➡️ Dror Haor on LinkedIn – https://www.linkedin.com/in/dror-haor-phd-77152322/ ➡️ Dror Haor on Twitter – https://x.com/DrorHaor 🌐 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

Federico Bacci, a data scientist and ML engineer at Bol, shares his expertise in deploying machine learning models for e-commerce forecasting. He delves into the importance of model explainability and feature engineering over mere model complexity. The discussion highlights the challenges of integrating feedback from stakeholders and the intricacies of demand forecasting. Federico argues that large language models aren't always the answer, advocating instead for tailored solutions that effectively address specific business needs.

Michał Oleszak, an ML engineering manager at Solera, talks about using ML in auto claims processing, challenges in deploying ML pipelines, data quality for computer vision tasks, and exciting developments in self-supervised learning. He also discusses monorepo architecture benefits, model evaluation, and the importance of statistics in ML.

In this episode, I chat with Ljubomir Buturovic, VP of ML and Informatics at Inflammatix. We discuss using ML to diagnose infections and blood tests in the emergency room. We dive into the challenges of building diagnostic (classification) and prognostic (predictive) modes, with takeaways related to building datasets for production use cases. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00 What is Inflammatix and how do they use ML7:32 Edge Device Deployment: The Future of Model Deployment21:16 Navigating Regulatory Submission for Medical Products 26:01 Evolution of Regulatory Processes in ML for Medical Applications30:18 Challenges and Solutions in ML for Medical Applications 34:00 The Future of AI in Clinical Care40:25 The Overrated Concept of Interpretability in AI and ML45:32 RecommendationsLinks 🌎📈 Our world in data: https://ourworldindata.org/ 🚀 Profiles of the future: https://www.amazon.com/Profiles-Future-Arthur-C-Clarke-ebook/dp/B00BY7GITK ➡️ Ljubomir Buturovic on LinkedIn – https://www.linkedin.com/in/ljubomir-buturovic-798156/ ➡️ Ljubomir Buturovic on Twitter – https://x.com/ljbuturovic 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn

Idan Gazit, Senior Director of Research at GitHub Next, talks about exploring strategic technologies, ML focus at GitHub Next, challenges of evaluating the impact of AI products, journey into AI space, testing AI products in smaller organizations, and the future of AI interfaces.

Uri Goren, CEO of Argmax, discusses the future of digital advertising post-cookies era. Topics include challenges in personalized ads, shift to contextual features, and predictions for AI/ML. They explore maintaining privacy and regulatory impacts on user identification methods.

Exploring the buzz around NLP and generative AI, mixing old school with new tech. Discussing the shift towards smarter databases and choosing the right LLM for applications. Debating the advantages, disadvantages, and dark side of LLMs. Predictions for the future of ML and AI, and recommendations for staying focused in an information-overloaded age.

Delving into AI in medical devices and medicine, Mila Orlovsky shares insights on practical applications, data challenges, and navigating regulatory standards. Topics include predictive analytics, business side learnings, impact of ML models in medicine, FDA compliance, future of ML and AI, and controversial predictions in the field. Essential listening for healthcare professionals looking to implement AI.