06-05-2023, 06:40 AM
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2020 | English | 9781492093626 | EPUB | 54 pages |
For years, organizations have struggled to move data science, machine learning, and AI projects from the realm of experimental to having real business impact. One reason is because pivoting operations around these technologies involves more than just technology--the orchestration of people and processes is also critically important. In the wake of the global health crisis, the need for structure around building and maintaining machine learning models (much less tens, hundreds, or thousands of them) has only grown.
With this report, business leaders will learn about MLOps, a process for generating long-term value while reducing the risk associated with data science, ML, and AI projects. Authors Lynn Heidmann and Mark Treveil from Dataiku start by introducing the data science-ML-AI project lifecycle to help you understand what--and who--drives these projects.
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