| Description |
1 online resource (41 pages) : illustrations |
| Bibliography |
Includes bibliographical references. |
| Summary |
The past few years have seen significant developments in data science, AI, machine learning, and advanced analytics. But the wider adoption of these technologies has also brought greater cost, risk, regulation, and demands on organizational processes, tasks, and teams. This report explains how ModelOps can provide both technical and operational solutions to these problems. Thomas Hill, Mark Palmer, and Larry Derany summarize important considerations, caveats, choices, and best practices to help you be successful with operationalizing AI/ML and analytics in general. Whether your organization is already working with teams on AI and ML, or just getting started, this report presents ten important dimensions of analytic practice and ModelOps that are not widely discussed, or perhaps even known. |
| Subject |
Business -- Data processing.
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Artificial intelligence -- Industrial applications.
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Machine learning -- Industrial applications.
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Project management -- Technological innovations.
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Gestion -- Informatique. |
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Intelligence artificielle -- Applications industrielles. |
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Apprentissage automatique -- Applications industrielles. |
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Gestion de projet -- Innovations. |
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Artificial intelligence -- Industrial applications |
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Business -- Data processing |
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Machine learning -- Industrial applications |
| Added Author |
Palmer, Mark, author.
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Derany, Larry, author.
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