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AI and Machine Learning: Enterprise Use Cases and Challenges

Watch this on-demand webinar to learn how you can accelerate your AI initiative and deliver faster time-to-value with machine learning.

AI has moved into the mainstream. Innovators in every industry are adopting machine learning for AI and digital transformation, with a wide range of different use cases. But these technologies are difficult to implement for large-scale distributed environments with enterprise requirements.

This webinar discusses:

-The game-changing business impact of AI and machine learning (ML) in the enterprise
-Example use cases: from fraud detection to medical diagnosis to autonomous driving
-The challenges of building and deploying distributed ML pipelines and how to overcome them
-A new turnkey solution to accelerate enterprise AI initiatives and large-scale ML deployments

Find out how to get up and running quickly with a multi-node sandbox environment for TensorFlow and other popular ML tools.
Recorded Jun 28 2018 61 mins
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Presented by
Radhika Rangarajan Director, Data Analytics and AI, Intel; Nanda Vijaydev Director, Solutions, BlueData
Presentation preview: AI and Machine Learning: Enterprise Use Cases and Challenges

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    Radhika Rangarajan Director, Data Analytics and AI, Intel; Nanda Vijaydev Director, Solutions, BlueData
    Watch this on-demand webinar to learn how you can accelerate your AI initiative and deliver faster time-to-value with machine learning.

    AI has moved into the mainstream. Innovators in every industry are adopting machine learning for AI and digital transformation, with a wide range of different use cases. But these technologies are difficult to implement for large-scale distributed environments with enterprise requirements.

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    -The game-changing business impact of AI and machine learning (ML) in the enterprise
    -Example use cases: from fraud detection to medical diagnosis to autonomous driving
    -The challenges of building and deploying distributed ML pipelines and how to overcome them
    -A new turnkey solution to accelerate enterprise AI initiatives and large-scale ML deployments

    Find out how to get up and running quickly with a multi-node sandbox environment for TensorFlow and other popular ML tools.
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    - How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
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    - What Machine Learning is in relation to AI and how it connects your data to find patterns
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    Learn a framework for creating and communicating a vision that describes the overall direction of your AI product, a defined product strategy, a cross-functional roadmap aligned with the strategy, and a list of metrics that track progress towards the strategy

    About the Speaker: Geordie Kaytes is the director of UX strategy for Boston-area UI/UX studio Fresh Tilled Soil and a partner at Heroic (https://www.heroicteam.com), a design leadership coaching firm that helps growing companies scale their digital product capabilities. A digital product design leader with deep experience in design process transformation and cross-functional expertise in design, strategy, and technology, Geordie has helped companies in a broad range of industries develop a 360-degree view of their product design processes. Previously, he did his obligatory tour of duty in management consulting. He holds a BA from Yale in political science. He is a coauthor of the Medium publication Radical Product.
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This channel covers the advent of artificial intelligence in business and society. Join the discussion with webinars and videos covering everything from neural networks, to computer vision and NLP, to machine learning and AI application in the real world.

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  • Title: AI and Machine Learning: Enterprise Use Cases and Challenges
  • Live at: Jun 28 2018 5:00 pm
  • Presented by: Radhika Rangarajan Director, Data Analytics and AI, Intel; Nanda Vijaydev Director, Solutions, BlueData
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