5 takeaways on scaling machine learning

Twitter and Facebook can teach us a lot about effective artificial intelligence

Many companies are just starting their machine learning journeys and 37% of organizations have implemented artificial intelligence according to a recent Gartner survey. If you’ve opened the door to machine learning, you might want to review 10 questions before starting a machine learning proof of concept or the complete guide to AI, machine learning, and deep learning.

Machine learning is evolving, with new commercial breakthroughs, scientific advancements, framework improvements, and best practices frequently reported.

We have a lot to learn from organizations that have large-scale machine learning programs and view artificial intelligence as core to their business. At the O’Reilly Artificial Intelligence Conference in New York last month I saw several common trends between Facebook’s and Twitter’s machine learning programs.

Understand business needs and competitive factors

At Facebook, machine learning is used in many areas. On the Facebook home page, it searches, translates language, scans the news feed, recognizes faces in uploaded photos, and sees what ads are presented. Behind the scenes, machine learning is used for content understanding, speech recognition, content integrity, sentiment analysis, objectionable content detection, and fraudulent account detection.

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