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Introducing MLOps : how to scale machine learning in the enterprise
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, ...
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| Kode Buku | : | 240013 |
| Kode Klasifikasi | : | 004 |
| Judul Buku | : | Introducing MLOps : how to scale machine learning in the enterprise |
| Edisi | : | 1 |
| Penulis | : | Mark Treveil & The Dataiku Team |
| Penerbit | : | Oreilly |
| Bahasa | : | Inggris |
| Tahun | : | 2021 |
| ISBN | : | 978-1-492-08329-0 |
| Tajuk Subjek | : | Ilmu komputer |
| Deskripsi | : | ix, 177 hlm. ; ind., 25 cm |
| Eksemplar | : | 4 |
| Stok | : | 4 |
| Petugas Input | : | Sofia Nurani, S.Hum |
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.
This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.
This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.
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