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According to Gartner, ModelOps lies at the center of any organizations’ enterprise AI strategy. Hear from Stu Bailey, co-founder and Chief Enterprise AI Architect, how ModelOp, leader in the ModelOps category, is helping its customers to operationalize all models across the enterprise in a world with models of all types, regulations and the need for explainability and visibility.
Scale AI, while staying compliant and achieving your business goals.

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Data-driven enterprises use data and insights to fundamentally change the value proposition they offer to customers. The best among them catalyze a virtuous cycle in which more customers generate more data that is used to further enrich products and services, creating a sustainable competitive advantage. This requires orchestrating process, culture, and technology around making the most of data’s distinctive–and disruptive–economics.

Attend this session to:
– Explore the key concepts that inspire data-driven enterprises
– See how your organization compares with benchmark data
– Learn how Apache Cassandra can accelerate your progress

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Traditional data governance had a principle aim of protecting the integrity of key business data for reporting, be it regulatory, financial or other strategic KPIs. This meant that focus was generally around data quality and lineage of critical data elements that could be found on strategic reports. But in the age of modern analytics, there is more urgency to use increasingly rawer data, creating an overwhelming task of managing and protecting potentially hundreds or even thousands of sources and fields to unlock value. This session will outline how modern data discovery and catalog techniques can be used to shift data governance further up the data funnel and how these techniques can accelerate the production of high value predictive models at speed in a Data Ops methodology.

• The role of the CDO is expanding from a defensive posture to a value driver
• Data Governance for AI and predictive modeling requires a new method of data governance anchored around easy but secure access to data in addition to aspects such as lineage and quality
• Data Ops methodology changes are key to delivering demonstrable value

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Organizations are pursuing predictive analytics to deliver greater value to customers, differentiate from their competitors, optimize business operations, and much more. Yet an overwhelming majority of machine learning & AI models fail to make it into production. During this session we’ll discuss:

• Best practices for deploying Machine Learning & AI models in production
• How companies are accelerating their predictive analytics projects
• The future of data warehouse architectures

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As organizations, we manage ever more data across an expanding heterogeneous landscape of clouds and data centers. This fragmentation as well as increasing (data privacy) regulation presents new business risks, drives operational costs and hampers the ability to gain insight and value from data.

In this session we’ll discuss how an enterprise data cloud delivers not just the analytics for the data life-cycle across hybrid cloud, but also how it provides consistent security and governance, regardless of where your data lives.

You will learn:
– The benefit of an enterprise data cloud
– The importance of defining a data strategy before a Cloud strategy
– How to securely gain access to self-service data and analytics

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