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What to Expect When You Are Putting AI in Production? - Dr. David Talby | ODSC East 2019
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Machine learning and “AI” systems often fail in production in unexpected ways. This video shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.
The covered topics include concept drift (identifying and correcting for model decay due to changes in the distribution of data in production), common pitfalls in A/B testing (like the primacy and novelty effects), offline versus online measurements, and systems that learn in production (such as adversarial learning use cases). This talk is intended for executives, technical leaders and product managers who want to learn from others’ mistakes how to best set up their teams & products for success.
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Also, You can visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops:
The covered topics include concept drift (identifying and correcting for model decay due to changes in the distribution of data in production), common pitfalls in A/B testing (like the primacy and novelty effects), offline versus online measurements, and systems that learn in production (such as adversarial learning use cases). This talk is intended for executives, technical leaders and product managers who want to learn from others’ mistakes how to best set up their teams & products for success.
Do You Like This Video? Share Your Thoughts in Comments Below
Also, You can visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops:
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