Past episodes listed in reverse chronological order. We have high-quality transcripts for a few episodes (see here).


104. Che SharmaModern Experimentation Platforms

103. Nic Hohn and Max PumperlaReinforcement Learning in Real-World Applications

102. Nikhil MuralidharMLOps Anti-Patterns

101.  Pardhu Gunnam and Mars LanWhy You Need a Modern Metadata Platform

100. Yoav ShohamMaking Large Language Models Smarter

99. Jeremy StanleyAI Begins With Data Quality

98. Michel TricotModernizing Data Integration

97.  Hamel HusainDeploying Machine Learning Models Safely and Systematically

96.  Bob FridayLarge-scale machine learning and AI on multi-modal data

95.  Viviana AcquavivaMachine Learning in Astronomy and Physics

94.  Viral ShahThe Unreasonable Effectiveness of Multiple Dispatch

93.  Jike Chong and Yue Cathy Chang in conversation with Jenn Webb and Ben LoricaHow To Lead In Data Science

92.  Paco Nathan in conversation with Jenn Webb and Ben LoricaWhy interest in graph databases and graph analytics are growing

91.  Tara Kelly in conversation with Jenn Webb and Ben LoricaThe State of Data Journalism

90.  Rayid Ghani and Andrew BurtAuditing machine learning models for discrimination, bias, and other risks

89.  Charles MartinAn oscilloscope for deep learning

88.  Jesse Anderson in conversation with Jenn Webb and Ben LoricaWhat’s new in data engineering

87.  Sean Taylor in conversation with Jenn Webb and Ben LoricaChanges to the data science role and to data science tools

86.  Steven Feng and Eduard HovyData Augmentation in Natural Language Processing

85.  Brad KingStorage Technologies for a Multi-cloud World

84.  Chris White in conversation with Jenn Webb and Ben LoricaTowards a next-generation dataflow orchestration and automation system

83.  Reza Hosseini and Albert ChenBuilding a flexible, intuitive, and fast forecasting library

82.  Sercan ArikNeural Models for Tabular Data

81.  Connor LeahyTraining and Sharing Large Language Models

80.  Paolo Cremonesi and Maurizio Ferrari DacremaQuestioning the Efficacy of Neural Recommendation Systems

79.  Hyun KimAutomation in Data Management and Data Labeling

78.  Nicolas HohnReinforcement Learning For the Win

77.  Andrew BurtHow Companies Are Investing in AI Risk and Liability Minimization

76.  Travis AddairThe Future of Machine Learning Lies in Better Abstractions

75.  Yonatan Geifman and Ran El-YanivWhy You Should Optimize Your Deep Learning Inference Platform

74.  Jerry Overton in conversation with Jenn Webb and Ben LoricaAI Beyond Automation

73.  Steve TouwInjecting Software Engineering Practices and Rigor into Data Governance

72.  Davit BuniatyanBuilding a data store for unstructured data and deep learning applications

71.  Zhe ZhangHow Technology Companies Are Using Ray

70.  Abe GongData quality is key to great AI products and services

69.  Parisa RashidiMachine Learning in Healthcare

68.  Simon Rodriguez in conversation with Jenn Webb and Ben LoricaMeasuring the Impact of AI and Machine Learning Research

67.   Ryan WisneskyThe Mathematics of Data Integration and Data Quality

66.  Jian PeiPricing Data Products

65.  Sharon Zhou in conversation with Jenn Webb and Ben LoricaChallenges, Opportunities, and Trends in EdTech

64.  Alex Wong and Sheldon FernandezTowards Simple, Interpretable, and Trustworthy AI

63.  Assaf Araki and Ben Lorica in conversation with Jenn WebbThe Rise of Metadata Management Systems

62.  Michael MahoneyTools for building robust, state-of-the-art machine learning models

61.  Sonal Goyal and Ben Lorica in conversation with Jenn WebbCreating Master Data at Scale with AI

60.  Bruno Fernandez-RuizBringing AI and computing closer to data sources

59.  Bharath RamsundarDeep Learning in the Sciences

58.  Ira CohenTaking business intelligence and analyst tools to the next level

57.   Omer DrorData exchanges and their applications in healthcare and the life sciences


56.   Ben Lorica and Mikio Braun in conversation with Jenn WebbKey AI and Data Trends for 2021

55.   Jesse Anderson and Ben Lorica in conversation with Jenn WebbA Unified Management Model for Successful Data-Focused Teams

54.   Dan Geer and Andrew BurtSecurity and privacy for the disoriented

53.   Rumman ChowduryThe State of Responsible AI

52.   Jack MorrisImproving the robustness of natural language applications

51.   Yishay CarmielEnd-to-end deep learning models for speech applications

50.   Ram ShankarSecuring machine learning applications

49.   Marco RibeiroTesting Natural Language Models

48.   Xiyin ZhouDetecting Fake News

47.   Neil ThompsonThe Computational Limits of Deep Learning

46.   Piero MolinoMaking deep learning accessible

45.   Mayank KejriwalBuilding and deploying knowledge graphs

44.   Murat ÖzbayoğluFinancial Time Series Forecasting with Deep Learning

43.   Viral ShahA programming language for scientific machine learning and differentiable programming

42.   Kira RadinskyUsing machine learning to modernize medical triage and monitoring systems

41.   Max PumperlaConnecting Reinforcement Learning to Simulation Software

40.   Weifeng ZhongUsing machine learning to detect shifts in government policy

39.   Ofer RazonWhat is AI Assurance?

38.   Alan NicholBest practices for building conversational AI applications

37.   Paco Nathan and Ben Lorica in conversation with Jenn WebbTools for scaling machine learning

36.   Joel GrusFrom Python beginner to seasoned software engineer

35.   Bruno GonçalvesAssessing Models and Simulations of Epidemic Infectious Diseases

34.   Karthik Ramasamy and Arun KejariwalImproving the hiring pipeline for software engineers

33.   Lauren KunzeHow to build state-of-the-art chatbots

32.   Ameet TalwalkarDemocratizing Machine Learning

31.   Denise GosnellHow graph technologies are being used to solve complex business problems

30.   Amy HeineikeMachines for unlocking the deluge of COVID-19 papers, articles, and conversations

29.   Christopher NguyenDesigning machine learning models for both consumer and industrial applications

28.   Matthew HonnibalBuilding open source developer tools for language applications

27.   Chris WigginsViewing machine learning and data science applications as sociotechnical systems

26.   Andrew BurtIdentifying and mitigating liabilities and risks associated with AI

25.   Arun Verma (in conversation with Jenn Webb) → How machine learning is being used in quantitative finance

24.   Harish DoddiUnderstanding machine learning model governance

23.   Wes McKinneyImproving performance and scalability of data science libraries

22.   Pete WardenWhy TinyML will be huge

21.   Evan SparksAn open source platform for training deep learning models

20.   Kenneth StanleyAlgorithms that continually invent both problems and solutions

19.   Bruno GonçalvesComputational Models and Simulations of Epidemic Infectious Diseases

18.   Robert MunroHuman-in-the-loop machine learning

17.   Chris NicholsonNext-generation simulation software will incorporate deep reinforcement learning

16.   Solmaz ShahalizadehBusiness at the speed of AI: Lessons from Shopify

15.   Edo LibertyHow deep learning is being used in search and information retrieval

14.   Alejandro SaucedoThe responsible development, deployment and operation of machine learning systems

13.   Edmon BegoliHyperscaling natural language processing

12.   Krishna GadeWhat businesses need to know about model explainability

11.   Dean WamplerScalable Machine Learning, Scalable Python, For Everyone

10.  Dafna ShahafComputational humanness, analogy and innovation, and soft concepts

9.    David TalbyBuilding domain specific natural language applications

8.    Morten DahlThe state of privacy-preserving machine learning

7.     Sijie GuoTaking messaging and data ingestion systems to the next level

6.    Bahman BahmaniBusiness at the speed of AI: Lessons from Rakuten

5.    Nir ShavitThe combination of the right software and commodity hardware will prove capable of handling most machine learning tasks


4.    Ben and Mikio BraunKey AI and Data Trends for 2020

3.    Rajat MongaThe evolution of TensorFlow and of machine learning infrastructure

2.    Reza ZadehBuilding large-scale, real-time computer vision applications

1.    Paco NathanTaking stock of foundational tools for analytics and machine learning