Super Data Science: ML & AI Podcast with Jon Krohn
Jon Krohn
The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.
Episodes
Mentioned books
Jul 7, 2023 • 8min
694: CatBoost: Powerful, efficient ML for large tabular datasets
Learn about CatBoost, a powerful tree-boosting algorithm that simplifies modeling tabular data. Discover how it handles categorical features, accelerates model training, and excels in classification, regression, ranking, and recommendation systems.
Jul 4, 2023 • 1h 20min
693: YOLO-NAS: The State of the Art in Machine Vision, with Harpreet Sahota
Data science expert Harpreet Sahota discusses machine vision, YOLO architectures, YOLO-NAS model, and developer relations with Jon Krohn. They explore the evolution of object detection models, neural architecture search, quantization methods, and challenges of deploying models on edge devices.
Jun 30, 2023 • 8min
692: Lossless LLM Weight Compression: Run Huge Models on a Single GPU
Discover the SPQR approach for lossless LLM weight compression, enabling running large models on a single GPU. Learn about QLora, combining low-rank adaptation and quantization for better performance
Jun 27, 2023 • 1h 35min
691: A.I. Accelerators: Hardware Specialized for Deep Learning
Join Ron Diamant, Senior Principal Engineer at AWS, as he delves into the world of AI accelerators, discussing GPUs vs CPUs, chip design, AWS's accelerators Trainium and Inferentia, model optimizations, chip production process, AWS Neuron SDK, and his journey into chip design.
Jun 23, 2023 • 26min
690: How to Catch and Fix Harmful Generative A.I. Outputs
Krishna Gade, CEO of Fiddler.AI, delves into managing harmful outputs in Generative AI models, discussing issues like inaccuracies, biases, and privacy risks. He emphasizes the need for monitoring AI to build trust and showcases Fiddler's explainability algorithms and bias detection tools as essential solutions.
Jun 20, 2023 • 1h 18min
689: Observing LLMs in Production to Automatically Catch Issues
ML Observability experts, Amber Roberts and Xander Song, discuss drift detection, retraining models, model biases, and fairness in AI development. They cover Arize's open-source product, Phoenix, and the importance of monitoring production models for issues and scalability challenges with LLMs.
Jun 16, 2023 • 14min
688: Six Reasons Why Building LLM Products Is Tricky
This podcast explores the challenges of building products with Large Language Models (LLMs) such as context window limitations, slow performance, and legal concerns, emphasizing the importance of intelligent product design.
Jun 13, 2023 • 1h 47min
687: Generative Deep Learning, with David Foster
David Foster, a data scientist specializing in generative AI for music, discusses topics like generative vs discriminative modeling, autoencoders, noise in generative AI, CLIP models, world models, and transformers. He explores the potential of generative AI in music composition and image generation, as well as the risks and progress of advanced AI models. Foster also explains the application of generative AI beyond music, such as text-to-image, text-to-code, and reinforcement learning.
Jun 9, 2023 • 30min
686: Open-Source "Responsible A.I." Tools, with Ruth Yakubu
Ruth Yakubu, Microsoft's expert on Responsible AI principles, talks about the open-source Responsible AI Toolbox for assessing model fairness, inclusiveness, and privacy. She discusses Microsoft's Responsible AI principles, the responsible AI toolkit, and the importance of visualizing and analyzing data thoroughly for fair treatment of all groups.
Jun 6, 2023 • 1h 6min
685: Tools for Building Real-Time Machine Learning Applications, with Richmond Alake
Richmond Alake, Machine Learning Architect at Slalom Build, discusses his startups, podcasting, and new course on feature stores. He emphasizes the importance of writing for data scientists and shares insights on building real-time ML applications, developing AI companions, and investing in MLOps. The episode covers his use of tools like Databricks, Kinesis, and Swift programming for ML production.
