컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
TFX는 프로덕션 ML 파이프라인을 배포하기 위한 엔드 투 엔드 플랫폼입니다
연구에서 프로덕션으로 모델을 이동할 준비가 되면 TFX를 사용하여 프로덕션 파이프라인을 만들고 관리하세요.
작동 방식
TFX 파이프라인은 확장 가능한 고성능 머신러닝 작업을 위해 특별히 설계된 ML 파이프라인을 구현하는 일련의 구성요소입니다. 구성요소는 TFX 라이브러리를 사용하여 빌드되며, 이는 개별적으로 사용할 수도 있습니다.
중급
Google Cloud에 호스팅된 TFX 파이프라인 만들기
Google Cloud에서 나만의 머신러닝 파이프라인을 제작할 수 있도록 도와주는 TFX 및 Cloud AI Platform 파이프라인을 소개합니다. 일반적인 ML 개발 과정을 따라 개발해 보세요. 데이터 세트 검사부터 시작해 제대로 작동하는 파이프라인을 완성할 수 있습니다.
[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)"]]
