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How to take machine learning from exploration to implementation

O'Reilly Media - Data

The results of a recent survey we conducted (with 11,000+ respondents, a full report is forthcoming) bears this out—only about 15% of respondents worked for companies that have extensive experience using ML in production: In this post, I’ll describe how one can go from “exploration and evaluation” to actual “implementation” of ML technologies.

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Software Outsourcing: Why CEOs Love It

Gorilla Logic

Monetize data with technologies such as artificial intelligence (AI), machine learning (ML), blockchain, advanced data analytics , and more. Establishing scalable and maintainable user and identity management, authentication, and authorization. Get the most out of your data with data warehousing and analytics.

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Software Engineering Daily: Feature Flags with Edith Harbaugh

LaunchDarkly

“We’re very laser-focused on making the developer extremely successful and happy and comfortable, comfortable that we’re reliable, comfortable that we’re scalable, comfortable that we can handle their load. You could pass us attributes from business analytics. ’ That’s very liberating to the developer.