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TFX, üretim ML işlem hatlarını dağıtmak için uçtan uca bir platformdur
Modellerinizi araştırmadan üretime taşımaya hazır olduğunuzda, bir üretim hattı oluşturmak ve yönetmek için TFX'i kullanın.
Nasıl çalışır
TFX işlem hattı, ölçeklenebilir, yüksek performanslı makine öğrenimi görevleri için özel olarak tasarlanmış bir ML işlem hattını uygulayan bir bileşenler dizisidir. Bileşenler, ayrı ayrı da kullanılabilen TFX kitaplıkları kullanılarak oluşturulmuştur.
Orta seviye
Google Cloud'da barındırılan TFX ardışık düzenleri oluşturun Google Cloud'da kendi makine öğrenimi ardışık düzenlerinizi oluşturmak için TFX ve Cloud AI Platform Ardışık Düzenlerine giriş. Veri kümesini inceleyerek başlayıp eksiksiz bir çalışma hattıyla biten tipik bir makine öğrenimi geliştirme sürecini izleyin.
Orta seviye
Cihaz üzerinde çıkarım için TensorFlow Lite ile TFX kullanın TFX'in cihaza dağıtılacak makine öğrenimi modellerini nasıl oluşturup değerlendirebileceğini öğrenin. TFX artık TFLite için yerel destek sağlıyor ve bu da mobil cihazlarda yüksek verimli çıkarımların gerçekleştirilmesini mümkün kılıyor.
[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)"]]
