Deploying production ML models with TensorFlow Serving overview 

Wei Wei, Developer Advocate at Google, overviews deploying ML models into production with TensorFlow Serving, a framework that makes it easy to serve the production ML models with low latency and high throughput. Learn how to start a TF Serving model server and send POST requests using the command line tool. Wei covers what it is, its architecture, general workflow, and how to use it.
Stay tuned for the upcoming episodes on Deploying Production ML models with TensorFlow Serving. Wei Wei will cover how to customize TF Serving, tune performance, perform A/B testing and monitoring, and more.
Resources:
TensorFlow Serving → goo.gle/3tLWkqr
TensorFlow Serving with Docker → goo.gle/3tQHyi0
Training and serving a TensorFlow model with TF Serving → goo.gle/3HE2e2F
Deploying Production ML Models with TensorFlow Serving playlist → goo.gle/tf-serving
Subscribe to TensorFlow → goo.gle/TensorFlow
#TensorFlow #MachineLearning #ML

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