Abstract
With the development of AI in technology and business, AI is no longer an experiment limited to a select few data scientists. It will penetrate all aspects of enterprise business operations and continue to innovate and optimize for new business scenarios. Now the focus shifts from the competition of AI algorithms to how to combine the strength of expert teams and AI technology for the actual needs of the enterprise and industry to generate business value.
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Notes
- 1.
See Reference [1] for more information on the Gartner Top Strategic Technology Trends for 2022.
- 2.
See References [2] and[3] for more information on reference models of the AI lifecycle.
- 3.
See Reference [4] for more information on service-level requirements.
- 4.
Please review Chapter 5.
- 5.
See Reference [5] for more information on DataOps.
- 6.
See Reference [6] for more information on the difference between ModelOps and MLOps.
- 7.
See Reference [7] for more information on the CDO 2021 study.
- 8.
See Chapter 8.
- 9.
See Reference [8] for more information on Gartner’s vision for data and analytics.
- 10.
See Reference [9] for more information on data and sample projects in IBM Cloud Pak for Data Gallery.
- 11.
See Reference [10] for more information on why Andrew Ng advocates for data-centric AI.
- 12.
See Reference [11] for more information on the impact of poor-quality data on business from Forbes.
- 13.
Please, refer to chapter 6 for an explanation of ROC, AUC, etc.
- 14.
RBFOpt is an open source library for black-box optimization with costly function evaluations.
- 15.
See Reference [12] for more information about the benefits AutoAI could bring to MLOps and the AI lifecycle.
- 16.
A/B testing is a method of comparing two versions of a web page or app.
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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Data Fabric and Data Mesh for the AI Lifecycle. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_9
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DOI: https://doi.org/10.1007/978-1-4842-9253-2_9
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