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Ten things to know about ModelOps : successful strategies /
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 Model...
| Call Number: | Libro Electrónico |
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| Main Authors: | , , |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Sebastopol, CA :
O'Reilly Media, Inc.,
[2022]
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| Edition: | First edition. |
| Subjects: | |
| Online Access: | Texto completo (Requiere registro previo con correo institucional) |
| 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. |
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| Physical Description: | 1 online resource (41 pages) : illustrations |
| Bibliography: | Includes bibliographical references. |