The Data Exchange Podcast: Krishna Gade on transparency and explainability in machine learning.
Scalable Machine Learning, Scalable Python, For Everyone
The Data Exchange Podcast: Dean Wampler on Ray, distributed systems, and Scala and Python.
Issue #4: Scaling Machine Learning, Lakehouses, and Learning from Experiments
Recommendations: Experimentation Works: The Surprising Power of Business Experiments. This is a book data scientists should give to their CxO.…
Computational humanness, analogy and innovation, and soft concepts
The Data Exchange Podcast: Dafna Shahaf on enabling computers to augment human cognition in novel ways.
What Is a Lakehouse?
by Ben Lorica, Michael Armbrust, Ali Ghodsi, Reynold Xin and Matei Zaharia. [This post originally appeared on the Databricks blog.]…
Building domain specific natural language applications
The Data Exchange Podcast: David Talby on Spark NLP and turning NLP research into enterprise solutions.
The state of privacy-preserving machine learning
The Data Exchange Podcast: Morten Dahl on TF Encrypted, federated learning, coopetitive learning, and other privacy tools for ML.
Taking messaging and data ingestion systems to the next level
The Data Exchange Podcast: Sijie Guo on how Apache Pulsar is able to handle both queuing and streaming, and both…
Issue #3: Trends, Business at the Speed of AI, and ML Tools
Recommendations: Inspired by Lauren Greenfield’s excellent documentary (“The Kingmaker”), I collected some quotes on disinformation that might prove handy in this election…
Business at the speed of AI: Lessons from Rakuten
The Data Exchange Podcast: Bahman Bahmani on attracting and retaining talent, and the importance of delivery-oriented teams.