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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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  • Architecting an Open Source Data Science Platform: 2018 Edition Oct 23 2018 5:00 pm UTC 60 mins
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  • State of the art natural language understanding in healthcare Aug 30 2018 5:00 pm UTC 60 mins
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  • Fight gaming fraud with AI and machine learning Jul 31 2018 5:00 pm UTC 60 mins
    Jeff Sakasegawa, Trust and Safety Architect, Sift Science
    Globally there are 2.2 billion active gamers, and 47 percent of them shell out cash while they play. And 100 percent of them are at risk from fraudsters who rip off everything from a gamer’s identity to their credit cards, online goods, and trust in your company. With every instance of fraud, your reputation takes a nose dive, driving away customers and directly impacting your bottom line.

    But fraud is notoriously difficult to combat. Legacy rules-based approaches have never been able to keep up with fraudsters, who constantly evolve their techniques using sophisticated technology like automated scripts and bots.

    That’s why machine learning and artificial intelligence are being leveraged to detect fraud before it affects your company and end users. Machine learning can sift through billions of game events and analyze vast streams of data in real time to stop fraud in its tracks.

    To learn more about how machine learning and AI can keep your game and players safe from increasingly aggressive online criminals, don’t miss this VB Live event!

    In this webinar, you'll learn:
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    * How account takeover, fake licensing, spam, and scams pose a particular challenge to gamers and gaming platforms
    * What policies your company should have in place around data breach ransom
    * How to combat trolling

    Speakers:
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    * Dean Takahashi, Lead Writer, GamesBeat
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    - How and when to use AI and Machine Learning in your financial institution

    About the presenter:

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  • AI and Machine Learning: Enterprise Use Cases and Challenges Recorded: Jun 28 2018 61 mins
    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.

    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.
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    With the General Data Protection Regulation (GDPR) becoming enforceable in the EU on May 25, 2018, many data scientists are worried about the impact that this regulation and similar initiatives in other countries that give consumers a "right to explanation" of decisions made by algorithms will have on the field of predictive and prescriptive analytics.

    In this session, Beau will discuss the role of interpretable algorithms in data science as well as explore tools and methods for explaining high-performing algorithms.

    Beau Walker has a Juris Doctorate (law degree) and BS and MS Degrees in Biology and Ecology and Evolution. Beau has worked in many domains including academia, pharma, healthcare, life sciences, insurance, legal, financial services, marketing, and IoT.
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    Gil Allouche, CEO, Metadata.io
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A journey of ideas and action from man to machine
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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