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TFX عبارة عن منصة شاملة لنشر خطوط أنابيب تعلم الآلة للإنتاج
عندما تكون جاهزًا لنقل نماذجك من البحث إلى الإنتاج، استخدم TFX لإنشاء مسار الإنتاج وإدارته.
كيف تعمل
خط أنابيب TFX عبارة عن سلسلة من المكونات التي تنفذ خط أنابيب ML والذي تم تصميمه خصيصًا لمهام التعلم الآلي القابلة للتطوير وعالية الأداء. يتم إنشاء المكونات باستخدام مكتبات TFX والتي يمكن استخدامها أيضًا بشكل فردي.
متوسط
تدريب وخدمة نموذج TensorFlow مع خدمة TensorFlow يقوم هذا الدليل بتدريب نموذج شبكة عصبية لتصنيف صور الملابس، مثل الأحذية الرياضية والقمصان، وحفظ النموذج المُدرب، ثم تقديمه باستخدام TensorFlow Serving. ينصب التركيز على خدمة TensorFlow، بدلاً من النمذجة والتدريب في TensorFlow.
متوسط
إنشاء خطوط أنابيب TFX مستضافة على Google Cloud مقدمة إلى TFX وCloud AI Platform Pipelines لإنشاء مسارات التعلم الآلي الخاصة بك على Google Cloud. اتبع عملية تطوير تعلم الآلة النموذجية، بدءًا من فحص مجموعة البيانات، وانتهاءً بمسار عمل كامل.
[null,null,[],[],[],null,["# TFX | ML Production Pipelines\n\nTFX is an end-to-end platform for deploying production ML pipelines\n===================================================================\n\nWhen you're ready to move your models from research to production, use TFX to create and manage a production pipeline. \n[Run Colab](https://colab.sandbox.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/tfx/penguin_simple.ipynb)\n\n\nGet started by exploring each built-in component of TFX.\n[View tutorials](/tfx/tutorials)\n\n\nLearn how to use TFX with end-to-end examples.\n[View the guide](/tfx/guide)\n\n\nGuides explain the concepts and components of TFX.\n[Explore addons](/tfx/addons)\n\n\nAdditional TFX components contributed by the community. \n\n### How it works\n\nA TFX pipeline is a sequence of components that implement an ML pipeline which is specifically designed for scalable, high-performance machine learning tasks. Components are built using TFX libraries which can also be used individually. \nIngest \\& validate data\n\n*** ** * ** ***\n\nExampleGen\n\n*** ** * ** ***\n\nIngests data into TFX pipelines and optionally splits the input dataset.\n[See guide](http://tensorflow.google.com/tfx/guide/examplegen) \n[ML Metadata](http://tensorflow.google.com/tfx/guide/mlmd) \nStatisticsGen\n\n*** ** * ** ***\n\nGenerates features statistics over both training and serving data.\n[See guide](http://tensorflow.google.com/tfx/guide/statsgen) \nSchemaGen\n\n*** ** * ** ***\n\nCreates schema by inferring types, categories, and ranges from the training data.\n[See guide](http://tensorflow.google.com/tfx/guide/schemagen) \nExampleValidator\n\n*** ** * ** ***\n\nIdentifies anomalies in training and serving data.\n[See guide](http://tensorflow.google.com/tfx/guide/exampleval) \n[TensorFlow Data Validation](http://tensorflow.google.com/tfx/guide/tfdv) \nTrain \\& analyze model\n\n*** ** * ** ***\n\nTransform\n\n*** ** * ** ***\n\nPerforms feature engineering on the dataset.\n[See guide](http://tensorflow.google.com/tfx/guide/transform) \n[TensorFlow Transform](http://tensorflow.google.com/tfx/transform/get_started) \nTuner\n\n*** ** * ** ***\n\nTunes the hyperparameters of the model.\n[See guide](http://tensorflow.google.com/tfx/guide/tuner) \nTrainer\n\n*** ** * ** ***\n\nTrains a TensorFlow model.\n[See guide](http://tensorflow.google.com/tfx/guide/trainer) \n[TensorFlow](http://tensorflow.google.com/tfx/guide/train) \nEvaluator\n\n*** ** * ** ***\n\nPerforms deep analysis of training results and helps validate exported models.\n[See guide](http://tensorflow.google.com/tfx/guide/evaluator) \nInfraValidator\n\n*** ** * ** ***\n\nChecks the model is actually servable from the infrastructure, and prevents bad models from being pushed.\n[See guide](http://tensorflow.google.com/tfx/guide/infra_validator) \n[TensorFlow Model Analysis](http://tensorflow.google.com/tfx/guide/tfma) \nDeploy in production\n\n*** ** * ** ***\n\nPusher\n\n*** ** * ** ***\n\nDeploys the model on a serving infrastructure.\n[See guide](http://tensorflow.google.com/tfx/guide/pusher) \n[TensorFlow Serving, TF Lite \\& TFJS](http://tensorflow.google.com/tfx/guide#deployment_targets) \n\nHow companies are using TFX\n---------------------------\n\n[See case studies](/about/case-studies?filter=TFX) \n[Spotify](https://labs.spotify.com/2020/01/16/for-your-ears-only-personalizing-spotify-home-with-machine-learning/) \n[Airbus](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html) \n[Gmail](https://security.googleblog.com/2020/02/improving-malicious-document-detection.html) \n[OpenX](https://blog.tensorflow.org/2021/02/how-openx-trains-and-serves-for-million-queries-per-second.html) \n\nSolutions to common problems\n----------------------------\n\nExplore step-by-step tutorials to help you with your projects. \nIntermediate\n[Train and serve a TensorFlow model with TensorFlow Serving](/tfx/tutorials/serving/rest_simple) \nThis guide trains a neural network model to classify images of clothing, like sneakers and shirts, saves the trained model, and then serves it with TensorFlow Serving. The focus is on TensorFlow Serving, rather than the modeling and training in TensorFlow. \nIntermediate\n[Create TFX pipelines hosted on Google Cloud](/tfx/tutorials/tfx/cloud-ai-platform-pipelines) \nAn introduction to TFX and Cloud AI Platform Pipelines to create your own machine learning pipelines on Google Cloud. Follow a typical ML development process, starting by examining the dataset, and ending up with a complete working pipeline. \nIntermediate\n[Use TFX with TensorFlow Lite for on-device inference](/tfx/tutorials/tfx/tfx_for_mobile) \nLearn how TFX can create and evaluate machine learning models that will be deployed on-device. TFX now provides native support for TFLite, which makes it possible to perform highly efficient inference on mobile devices. \n\nNews \\& announcements\n---------------------\n\nCheck out our [blog](https://blog.tensorflow.org/search?label=TFX&max-results=20) and [YouTube playlist](https://goo.gle/tfx-youtube) for additional TFX content, \nand subscribe to our TensorFlow newsletter to get the \nlatest announcements sent directly to your inbox. \n[Sign up](/subscribe) \n\nCommunity participation\n-----------------------\n\nSee more ways to participate in the TensorFlow community. \n[Community](/community) \n[TFX on GitHub](https://github.com/tensorflow/tfx) \n[ML Metadata](https://github.com/google/ml-metadata) [TensorFlow Data Validation](https://github.com/tensorflow/data-validation) [TensorFlow Transform](https://github.com/tensorflow/transform) [TensorFlow Model Analysis](https://github.com/tensorflow/model-analysis) [TensorFlow Serving](https://github.com/tensorflow/serving) \n[Stack Overflow](https://stackoverflow.com/questions/tagged/tfx) \n[ML Metadata](https://stackoverflow.com/questions/tagged/mlmd) [TensorFlow Data Validation](https://stackoverflow.com/questions/tagged/tensorflow-data-validation) [TensorFlow Transform](https://stackoverflow.com/questions/tagged/tensorflow-transform) [TensorFlow Model Analysis](https://stackoverflow.com/questions/tagged/tensorflow-model-analysis) [TensorFlow Serving](https://stackoverflow.com/questions/tagged/tensorflow-serving) \n[Issues, bug reports, and feature requests](https://github.com/tensorflow/tfx/issues) \n[ML Metadata](https://github.com/google/ml-metadata/issues) [TensorFlow Data Validation](https://github.com/tensorflow/data-validation/issues) [TensorFlow Transform](https://github.com/tensorflow/transform/issues) [TensorFlow Model Analysis](https://github.com/tensorflow/model-analysis/issues) [TensorFlow Serving](https://github.com/tensorflow/serving/issues) \n[Ask a question on TensorFlow Forum](https://discuss.tensorflow.org/tag/tfx) \n[Join the TFX-Addons Special Interest Group](https://github.com/tensorflow/tfx-addons) \n[Explore Dev Library community projects](https://devlibrary.withgoogle.com/products/ml) \n\nGet started with TFX\n--------------------\n\n[Explore tutorials](/tfx/tutorials)"]]
