TDWI
Businesses today need fast, scalable, and agile data and analytics, and cloud-based solutions are proving critical to satisfying these requirements. They enable organizations to rapidly and easily spin up systems and services for collecting, managing, and analyzing data. More important, cloud-based solutions deliver value from “data gravity”the surging volumes of new data created in the cloud by social media, the IoT, multichannel customer behavior, and other activity.
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Many of the most exciting innovations and advancements in data management today are occurring within the semantic layer of data architectures. For example, we’re witnessing new or improved approaches to semantic modeling, data cataloging, data lineage, and more. Even older forms of semantics—such as metadata and virtualization—are being infused with new techniques for augmentation and automation, including intelligent tool algorithms driven by machine learning and the use of graph analytics to generate data maps and automatically document data elements found via graph.
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As data practitioners and product managers strive to gain actionable insights with the data at hand, it’s important for both roles to work collaboratively.
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Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of cnvrg.io will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.
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