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When is data too clean to be useful for enterprise AI?

CIO

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Data 211
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Interpreting predictive models with Skater: Unboxing model opacity

O'Reilly Media - Data

Data Scientist Cathy O’Neil has recently written an entire book filled with examples of poor interpretability as a dire warning of the potential social carnage from misunderstood models—e.g., There is also a trade off in balancing a model’s interpretability and its performance.

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The Good and the Bad of Apache Kafka Streaming Platform

Altexsoft

After trying all options existing on the market — from messaging systems to ETL tools — in-house data engineers decided to design a totally new solution for metrics monitoring and user activity tracking which would handle billions of messages a day. We describe information search on the Internet with just one word — ‘google’.

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Glass rethinks the smartphone camera through an old-school cinema lens

TechCrunch

Smartphone cameras have gotten quite good, but it’s getting harder and harder to improve them because we’ve pretty much reached the limit of what’s possible in the space of a cubic centimeter. It may not be obvious that cameras won’t get better, since we’ve seen such advances in recent generations of phones.

Film 246
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How to Launch a Successful Product: Timing, Roles, and Product Launch Checklist 

Altexsoft

Maybe a team didn’t prioritize the features and lost a lot of time and money fixing too many mistakes. Maybe a team didn’t prioritize the features and lost a lot of time and money fixing too many mistakes. Or a developer failed to test the app with real users to verify usage scenarios, hoping his idea will take off by itself.

How To 131
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Book review: Accelerate

Henrik Warne

DevOps in this context means things like continuous delivery, automated tests, trunk-based development, and proactive monitoring of system health. Many aspects of software development are hard to measure. I really liked that the authors have thought hard about what to measure in order to get objective yet useful metrics.

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What you need to know about product management for AI

O'Reilly Media - Ideas

You already know the game and how it is played: you’re the coordinator who ties everything together, from the developers and designers to the executives. Why AI software development is different. You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door.