Abstract
At the heart of a Data Fabric and Data Mesh is the use of artificial intelligence (AI) and machine learning (ML) technologies to automate complex data tasks to the greatest extent possible. Therefore, understanding the concepts of AI and ML is the foundation for implementing both concepts in an enterprise. If you are already an AI/ML practitioner, you might skip this chapter. If you are not sure, please take a quick assessment by answering the following questions, and please continue with this chapter should you need more clarity:
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Notes
- 1.
See Reference [1] for more details on N-shots learning.
- 2.
See Reference [2] for more details on transfer learning.
- 3.
See Reference [3] for more details on federated learning.
- 4.
See Reference [4] for more details on semi-supervised learning.
- 5.
See Reference [5] for IBM’s definition of DL.
- 6.
See Reference [6] for more information on data preparation.
- 7.
See Reference [7] for more information on descriptive statistics.
- 8.
See Reference [8] for more information on feature selection.
- 9.
See Reference [9] for a comprehensive review on choosing the right ML algorithm.
- 10.
See Reference [10] for more information on deploying AI, especially in regard to developing scorecards and performing comprehensive self-assessments.
- 11.
See Reference [11] for more information on PMML, ONNX, and PFA.
- 12.
See Reference [10] for the IBM Data Science Best Practices on deployment architecture.
- 13.
See Reference [12] for more details on one-hot encoding.
- 14.
See Reference [13] for more information on Woodside’s story.
- 15.
See Reference [14] for more information on Woodside Energy leveraging IBM Watson.
- 16.
See Reference [15] for more information on NLP use cases.
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© 2023 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature
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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Relevant ML and DL Concepts. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_6
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DOI: https://doi.org/10.1007/978-1-4842-9253-2_6
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