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Practical Deep Learning at Scale with MLflow : Bridge the Gap Between Offline Experimentation and Online Production /
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow Key Features Focus on deep learning models and MLflow to develop practical business AI solutions at scale Ship deep learning pipelines from experim...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2022.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Table of Contents Deep Learning Life Cycle and MLOps Challenges Getting Started with MLflow for Deep Learning Tracking Models, Parameters, and Metrics Tracking Code and Data Versioning Running DL Pipelines in Different Environments Running Hyperparameter Tuning at Scale Multi-Step Deep Learning Inference Pipeline Deploying a DL Inference Pipeline at Scale Fundamentals of Deep Learning Explainability Implementing DL Explainability with MLflow.