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The Model-Driven Business

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  • How Leading Organizations are Solving the ModelOps Challenge
    How Leading Organizations are Solving the ModelOps Challenge
    Mike Gualtieri - VP, Principal Analyst, Forrester, Sami Cheong, Data Scientist at Bayer, Mac Steele, Domino Recorded: Mar 5 2019 49 mins
    Data science and the impact it will have on business and society has never been more hyped. Yet the reality is very few organizations today are getting business value from their data science (or AI) investments. A recent study shows that while 85% believe AI will allow their companies to obtain or sustain a competitive advantage, only 5% are using AI extensively. The reason for this gap is because it’s extremely hard for companies to get models (the core of many AI solutions) beyond their data science teams and into production.

    In this webinar, Forrester principal analyst Mike Gualtieri and Bayer data scientist Sami Cheong join Domino Director of Product Mac Steele to discuss the underlying causes of this gap and how leading organizations are solving their ModelOps challenges. Join the webinar to learn:

    Why technology is just one of the barriers holding companies back from realizing the value of their data science investment
    How Domino’s latest 3.0 release addresses the ModelOps challenge
    How Bayer’s data science organization uses Domino to develop and deliver model products to their global business
    There will be a short demonstration of Domino’s data science platform in addition to practical tips from an industry expert and leading practitioner.
  • Building a High-Throughput Data Science Machine
    Building a High-Throughput Data Science Machine
    Erik Andrejko, VP of Science at The Climate Corporation Recorded: Mar 5 2019 34 mins
    Scaling is hard. Scaling data science is extra hard. What does it take to run a sophisticated data science organization? What are some of the things that need to be on your mind as you scale to a repeatable, high-throughput data science machine?

    Erik Andrejko, VP of Science at The Climate Corporation, has spent a number of years focused on this problem, building and growing multi-disciplinary data science teams.

    In this video, we discuss what is critical to continue building world-class data science teams. We also discuss the practice of data science, the scaling of organizations, and key components and best practices of a data science project.
  • Managing Data Science at Scale
    Managing Data Science at Scale
    Dan Enthoven, Domino Data Lab Recorded: Mar 5 2019 48 mins
    Through working with companies ranging from agile startups to the Fortune 500, Domino has been able to curate use cases and learnings from these organizations about the challenges and successes of growing data science teams.

    In this webinar, we’ll share some of those learnings and how we see the market approach challenges. The range of topics include:

    -Goals for The Data Science Program
    -Challenges
    -Diagnosis
    -Project and System Recommendations
    -Tips for Managing Data Science in Domino
    -Conclusion with Q&A
  • Moody's Analytics Accelerates Modeling with Domino Data Lab & AWS
    Moody's Analytics Accelerates Modeling with Domino Data Lab & AWS
    Pratap Ramamurthy - Partner Solutions Architect, AWS; Mac Steele - Director of Product, Domino, Jacob Grotta - Moody's Recorded: Mar 4 2019 50 mins
    Over the last several years, financial institutions have had to delicately balance managing demands from regulators and stakeholders. Increased governance and need for transparency around custom credit risk and finance analytics have prompted institutions to focus investments in such areas.

    Learn how Moody’s Analytic’s centralized, cloud-based data science platform is empowering new, customized client solutions that are pushing the envelope in the financial services market.

    You’ll find out:

    -Why Moody’s Analytics chose a central, cloud-based data science platform.
    -How to bring a software engineering discipline to data science without constraining innovation.
    -How data scientists at Moody’s Analytics are aligning with business goals and improving competitive differentiation.
    -Why data-driven companies should be worried about data scientists’ Shadow IT.

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