Come to MLW 2024 – June 4-7 in Phoenix – and receive a free copy of the new breakthrough book, The AI Playbook, written by the founder of MLW, Eric Siegel.
Note: The AI Playbook publishes today! Click here for details and to order.
Following up on his bestselling Predictive Analytics, Siegel's new book presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment – bizML. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms.
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MLW 2024 includes four keynotes that directly relate to the book:
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1. A keynote from Siegel himself, "The BizML Playbook for Getting Machine Learning Deployed."
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2. Jack Levis led UPS to optimize the delivery of 16M packages a day by predicting tomorrow's deliveries. This story is a leading example within The AI Playbook. His MLW keynote is entitled, "10 Steps to Innovation – It's Math and Psychology."
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3. Unilever's Global VP of Data & Analytics Morgan Vawter provided the foreword for The AI Playbook. Her MLW keynote is entitled, "AI into Business Impact at Global Scale."
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4. FICOS's CAO Scott Zoldi leads the team that delivers the widely-adopted fraud detection system called Falcon – which screens in real-time every transaction for most of the world's payment cards. This story is a leading example within The AI Playbook. However, his MLW keynote addresses another topic, "Responsible AI: A Practical Guide."
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Moreover, MLW 2024 delivers an entire track on bizML – the business practice for ML operationalization – which is the very topic of The AI Playbook. The program also features an on-point expert panel session, "How to Engage and Deeply Collaborate with Business Stakeholders on ML Projects."
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Come to MLW 2024 to ramp up on best practices to get your ML models successfully deployed and improve enterprise ML's deployment track record.
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Free book: Event registrants will receive a copy of MLW Founder Eric Siegel's new book, The AI Playbook, which presents a six-step practice called bizML that ushers ML projects from conception to deployment.
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