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Enabling Enterprises to Operationalize AI and Machine Learning

Global spending on AI is increasing, but organizations don’t seem to see the ROI. Our white paper explores the gaps between AI’s hype and reality and what organizations can do to successfully operationalize their AI and machine learning projects.

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Global spending on AI is increasing, but organizations don’t seem to see the ROI. Our white paper explores the gaps between AI’s hype and reality and what organizations can do to successfully operationalize their AI and machine learning projects.

Many organizations found meaningful wins in adopting DevOps and tried to apply the same principles to optimize processes for data, analytics, and AI. However, many of them underestimated what it takes to sow true gains and ended up unable to bring their vision into fruition.

We explore:

  • The common organizational and technical challenges in implementing AI and machine learning projects and a “back-to-basics” approach to overcoming them.
  • How Lingaro successfully set up its MLOps practice.
  • Real-life success stories of companies that took their AI projects into production.
Peek inside
Gartner, Predicts 2022: Analytics, BI and Data Science Ecosystems Drive New Perspectives, Peter Krensky, Rita Sallam, Farhan Choudhary, Julian Sun, David Pidsley, Carlie Idoine, Gareth Herschel, Soyeb Barot, Erick Brethenoux, Donald Feinberg, Andrew White, 9 December 2021

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.
Gartner, Data Hubs, Data Lakes and Data Warehouses: How They Differ and Are Used in Supply Chain, Christian Titze, Noha Tohamy, 19 October 2021

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.