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Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter Notebook environment that requires no setup, is free to use, and runs entirely in the cloud. Colaboratory notebooks can be shared just as you would with Google Docs or Sheets.Go to quickstart arrow_forward
Get a repository of open data curated by Google engineers and supported by domain experts from around the world. Use these data to build and test your algorithms before deployment or join with other datasets to unlock new insights. The data are hosted in BigQuery and Cloud Storage, making them simple to build on and use.View documentation arrow_forward
Cloud Deep Learning VM Image Beta
Deep Learning VM Image is pre-configured Compute Engine images for popular machine learning frameworks such as TensorFlow, scikit-learn, and PyTorch.View documentation arrow_forward
Cloud Pub/Sub is a simple, reliable, scalable foundation for large-scale stream analytics and event-driven computing systems. As part of Google Cloud’s stream analytics solution, the service ingests event streams and delivers them to Cloud Dataflow for processing and BigQuery for analysis as a data warehousing solution.View documentation arrow_forward
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Cloud Dataprep is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. Cloud Dataprep is serverless and works at any scale — there’s no infrastructure to deploy or manage.View documentation arrow_forward
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Get insights from your data faster without needing to copy or move it. BigQuery gives you full view of all your data by seamlessly querying data stored in BigQuery’s managed columnar storage, Cloud Storage, Cloud Bigtable, Google Sheets, and Google Drive.Explore tutorials arrow_forward
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Cloud Datalab is an interactive tool built on Jupyter (formerly iPython) created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.Go to Quickstart arrow_forward
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Cloud ML Engine
Add an extra layer of intelligence to your pipeline by running the event streams through custom TensorFlow, XGBoost, or scikit-learn machine learning models.View training overview arrow_forward
TensorFlow™ is an open source software library for high-performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.View documentation arrow_forward
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Hardware accelerators on Google Cloud offer the flexibility to choose the right accelerator for the best performance per dollar on ML workloads. Select from a portfolio of accelerators to run your workloads for training and predictions.Cloud TPU arrow_forward
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Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive.Explore facets arrow_forward
The Kubeflow project is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. The goal is not to re-create other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. You should be able to run Kubeflow anywhere you’re running Kubernetes.Read blog post arrow_forward
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Cloud ML Engine
Cloud ML Engine offers online prediction and batch prediction services for different ML frameworks. Data scientists can easily deploy models that have been trained anywhere into production without Docker containers or any special stitch-and-fix mechanisms. Online prediction supports frameworks like scikit-learn, XGBoost, Keras, and TensorFlow to serve classification, regression, clustering, and dimensionality reduction models.Get prediction overview arrow_forward
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