{"id":4865,"date":"2020-04-19T19:44:46","date_gmt":"2020-04-19T19:44:46","guid":{"rendered":"https:\/\/www.testpreptraining.com\/tutorial\/?page_id=4865"},"modified":"2020-04-19T19:44:46","modified_gmt":"2020-04-19T19:44:46","slug":"ai-platform-overview-google-professional-data-engineer-gcp","status":"publish","type":"page","link":"https:\/\/www.testpreptraining.ai\/tutorial\/ai-platform-overview-google-professional-data-engineer-gcp\/","title":{"rendered":"AI Platform Overview Google Professional Data Engineer GCP"},"content":{"rendered":"<ul>\n<li>The diagram shows high-level overview of ML workflow stages.<\/li>\n<li>The blue-filled boxes indicate where AI Platform provides managed services and APIs:<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-4680 aligncenter\" src=\"https:\/\/www.testpreptraining.ai\/tutorial\/wp-content\/uploads\/2020\/04\/Professional-Data-Engineer-Google-Cloud-image021-750x325.png\" alt=\"\" width=\"750\" height=\"325\" srcset=\"https:\/\/www.testpreptraining.ai\/tutorial\/wp-content\/uploads\/2020\/04\/Professional-Data-Engineer-Google-Cloud-image021-750x325.png 750w, https:\/\/www.testpreptraining.ai\/tutorial\/wp-content\/uploads\/2020\/04\/Professional-Data-Engineer-Google-Cloud-image021.png 774w\" sizes=\"auto, (max-width: 750px) 100vw, 750px\" \/>AI Platform can manage the following stages in the ML workflow:<\/p>\n<ul>\n<li>Train an ML model on data:\n<ul>\n<li>Train model<\/li>\n<li>Evaluate model accuracy<\/li>\n<li>Tune hyperparameters<\/li>\n<\/ul>\n<\/li>\n<li>Deploy trained model.<\/li>\n<li>Send prediction requests to model:\n<ul>\n<li>Online prediction<\/li>\n<li>Batch prediction (for TensorFlow only)<\/li>\n<\/ul>\n<\/li>\n<li>Monitor the predictions on an ongoing basis.<\/li>\n<li>Manage models and model versions.<\/li>\n<\/ul>\n<h3>Components of AI Platform<\/h3>\n<ul>\n<li>A Training service:<\/li>\n<li>allows you to train models using a wide range of different customization options.<\/li>\n<li>can select\n<ul>\n<li>many different machine types<\/li>\n<li>power training jobs,<\/li>\n<li>enable distributed training, use hyperparameter tuning,<\/li>\n<li>accelerate with GPUs and TPUs.<\/li>\n<\/ul>\n<\/li>\n<li>can select different ways to customize training application.<\/li>\n<li>can submit input data for AI Platform to train<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Prediction service:<\/p>\n<ul>\n<li>allows to serve predictions based on a trained model,<\/li>\n<li>whether or not the model was trained on AI Platform.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Notebooks:<\/p>\n<ul>\n<li>enables you to create and manage VM instances<\/li>\n<li>Instances are pre-packaged with JupyterLab.<\/li>\n<li>Also has deep learning packages, like TensorFlow and PyTorch<\/li>\n<li>can configure either CPU-only or GPU-enabled instances<\/li>\n<li>instances are protected by Google Cloud authentication and authorization<\/li>\n<li>can easily sync notebook with a GitHub repository.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Data labeling service:<\/p>\n<ul>\n<li>lets you request human labeling for a dataset to use to train a custom machine learning model.<\/li>\n<li>You can submit a request to label video, image, or text data.<\/li>\n<li>provide a representative sample of labeled data, specify all possible labels for dataset, and provide some instructions for how to apply those labels.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>Deep learning VM image:<\/p>\n<ul>\n<li>It lets you choose from a set of Debian 9-based machine images<\/li>\n<li>optimized for data science and machine learning tasks.<\/li>\n<li>All come with key ML frameworks and tools pre-installed<\/li>\n<li>can be used on instances with GPUs<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>AI Platform Deep Learning Containers<\/p>\n<ul>\n<li>are a set of Docker containers<\/li>\n<li>have data science frameworks, libraries, and tools pre-installed.<\/li>\n<\/ul>\n<p>Deep Learning Containers images include the following:<\/p>\n<p>Frameworks:<\/p>\n<ul>\n<li>TensorFlow<\/li>\n<li>TensorFlow 2.0<\/li>\n<li>PyTorch<\/li>\n<li>scikit-learn<\/li>\n<li>R<\/li>\n<\/ul>\n<p>Python, including these packages:<\/p>\n<ul>\n<li>numpy<\/li>\n<li>sklearn<\/li>\n<li>scipy<\/li>\n<li>pandas<\/li>\n<li>nltk<\/li>\n<li>pillow<\/li>\n<li>many others<\/li>\n<\/ul>\n<p>Nvidia packages with the latest Nvidia driver for GPU-enabled instances:<\/p>\n<ul>\n<li>CUDA 10.0<\/li>\n<li>CuDNN 7.*<\/li>\n<li>NCCL 2.*<\/li>\n<\/ul>\n<p>JupyterLab<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>Tools to interact with AI Platform<\/h3>\n<p>Google Cloud Console:<\/p>\n<ul>\n<li>deploy models to the cloud and manage models, versions, and jobs on the Cloud Console.<\/li>\n<li>gives a UI for working with machine learning resources.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>The gcloud command-line tool:<\/p>\n<ul>\n<li>manage models and versions, submit jobs, and accomplish other AI Platform tasks<\/li>\n<li>recommended for most AI Platform tasks<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>REST API:<\/p>\n<ul>\n<li>provides RESTful services for managing jobs, models, and versions, and for making predictions<\/li>\n<li>use the Google APIs Client Library for Python to access the APIs.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The diagram shows high-level overview of ML workflow stages. The blue-filled boxes indicate where AI Platform provides managed services and APIs: &nbsp; AI Platform can manage the following stages in the ML workflow: Train an ML model on data: Train model Evaluate model accuracy Tune hyperparameters Deploy trained model. Send prediction requests to model: Online&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[617],"tags":[727,619,623,726,622,618,621],"class_list":["post-4865","page","type-page","status-publish","hentry","category-google-gcp","tag-ai-platform-overview","tag-data-engineer","tag-gcp","tag-google-ai-platform-formerly-cloud-ml-engine","tag-google-certification","tag-google-cloud","tag-professional-data-engineer"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI Platform Overview Google Professional Data Engineer GCP - Testprep Training Tutorials<\/title>\n<meta name=\"description\" content=\"Google Cloud Certified Professional Data Engineer Tutorial, dumps, brief notes on AI Platform Overview\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.testpreptraining.ai\/tutorial\/ai-platform-overview-google-professional-data-engineer-gcp\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Platform Overview Google Professional Data Engineer GCP - 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