Скачать или смотреть Model deployment tensorflow model pipeline deep dive part 1

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okay, let's embark on a deep dive into tensorflow model deployment pipelines. this is part 1, focusing on foundational concepts and setting up a basic tensorflow serving pipeline. we'll cover:

*part 1: foundations and basic tensorflow serving*

1. *introduction to model deployment*
2. *tensorflow serving: overview*
3. *preparing your tensorflow model (saving in savedmodel format)*
4. *setting up tensorflow serving (docker)*
5. *serving the model*
6. *making inference requests*
7. *error handling and basic monitoring*
8. *code example: mnist image classification*
9. *next steps and further learning*

*1. introduction to model deployment*

model deployment is the process of making a trained machine learning model available for use in a production environment. this involves several critical steps:

*model packaging:* formatting the model in a way that it can be loaded and served efficiently.
*serving infrastructure:* choosing and configuring a system that can host the model, handle requests, and provide predictions.
*api design:* defining the interface (api) clients use to interact with the model.
*monitoring and management:* tracking model performance, resource usage, and identifying potential issues.
*versioning and rollouts:* managing different versions of the model and deploying updates safely.

*why is model deployment important?*

*real-world impact:* deploying allows your model to generate value by making predictions on real-world data.
*scalability:* enables your model to handle increasing numbers of requests.
*automation:* automates the prediction process, eliminating manual intervention.
*feedback loop:* allows you to collect data from real-world usage to improve your model.

*2. tensorflow serving: overview*

tensorflow serving (tfs) is a flexible, high-performance serving system designed specifically for deploying tensorflow models (although it can also serve othe ...

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Model deployment
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TensorFlow Serving
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