Taking stock of foundational tools for analytics and machine learning

The Data Exchange Podcast: Paco Nathan on data governance, AutoML, notebooks, and deep learning libraries.

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In this episode of the Data Exchange, I speak with Paco Nathan, author, teacher, and founder of Derwen.ai, a boutique consulting firm specializing in Data, ML, and AI. Paco consults with companies and speaks before audiences all over the world, and I plan to have him as a frequent guest on this podcast to draw on his observations of diverse organizations.

This particular conversation spanned many topics, including:

• Data Governance: Paco’s talk on the topic.
• AutoML: Paco’s talk on the topic.
• PyTorch and TensorFlow: links to the posts we discussed — [1], [2]
• The Bank of England report: “Machine learning in UK financial services”
• Reproducibility and feature selection
• The Streamlit open source project for ML app development, and Grus Law (“if you can think up something crazy and/or dangerous to do with notebooks, people are doing it.”)

My collaborators and I want this to be more than just a podcast. We want to create a community to help people make better decisions. A key part of this is getting you involved. We have ideas on how this community will grow, but as a first step, I want to ask a question related to one of the topics that Paco and I discussed: PyTorch and TensorFlow. We’d love to have you weigh in by filling out the form below. We will report on results and key insights in a future episode of this podcast:

[Survey question can also be found here:  https://bit.ly/2oZyIk0  ]

Related content:

•  Jesse Anderson and Paco Nathan on “What machine learning engineers need to know”
• “Becoming a machine learning company means investing in foundational technologies”
• “Managing machine learning in the enterprise: Lessons from banking and health care”

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