Managing the full TensorFlow training, tracking, and deployment lifecycle with MLflow (sponsored by Databricks)
"MLflow is an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. Clemens Mewald (Databricks) offers an overview of the latest component of MLflow, a model registry that provides a collaborative hub where teams ca...
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| Format: | Online Video |
| Language: | Inglés |
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[Place of publication not identified] :
O'Reilly Media
2020.
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| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822837106719 |
| Summary: | "MLflow is an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. Clemens Mewald (Databricks) offers an overview of the latest component of MLflow, a model registry that provides a collaborative hub where teams can share ML models, work together from experimentation to online testing and production, integrate with approval and governance workflows, and monitor ML deployments and their performance. You'll learn how to manage the full deployment lifecycle of TensorFlow models, from training to staging, A/B testing, and deployment to TensorFlow Serving."--Resource description page. |
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| Item Description: | Title from resource description page (viewed July 21, 2020). This session is from the 2019 O'Reilly TensorFlow World Conference in Santa Clara, CA and is sponsored by Databricks. |
| Physical Description: | 1 online resource (1 streaming video file (35 min., 58 sec.)) : digital, sound, color |
