TFX : production ML pipelines with TensorFlow

TFX production ML pipelines with TensorFlow

"ML development often focuses on metrics, delaying work on deployment and scaling issues. ML development designed for production deployments typically follows a pipeline model with scaling and maintainability as inherent parts of the design. Robert Crowe and Charles Chen (Google) takes a deep d...

Descripción completa

Detalles Bibliográficos
Otros Autores: Crowe, Robert, on-screen presenter (onscreen presenter), Chen, Charles, on-screen presenter
Formato: Vídeo online
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly Media 2020.
Materias:
Machine learning.
Artificial intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820445606719
Descripción
Sumario:"ML development often focuses on metrics, delaying work on deployment and scaling issues. ML development designed for production deployments typically follows a pipeline model with scaling and maintainability as inherent parts of the design. Robert Crowe and Charles Chen (Google) takes a deep dive into TensorFlow Extended (TFX), the open source version of the ML infrastructure platform that Google has developed for its own production ML pipelines."--Resource description page.
Notas:Title from resource description page (viewed July 22, 2020).
Descripción Física:1 online resource (1 streaming video file (41 min., 45 sec.)) : digital, sound, color

Ejemplares similares