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The main parts of the Jupyter Notebooks are: Metadata, Notebook format and list of cells. Metadata is a data Dictionary of definitions to set up and display the notebook. Notebook Format is a version number of the software. List of cells are different types of Cells for Markdown (display), Code (to execute), and output of the code type cells. [23]
[25] [26] In July 2021, Google Drive for Desktop, a new app for Windows and Mac, was released replacing "Backup and Sync" and "Drive File Stream". [27] Google Drive for desktop based on File Stream, which will support features previously exclusive to each respective Client. [26] Google stopped supporting Backup and Sync as of October 1, 2021. [28]
Google Notebook was a free online application offered by Google that allowed users to save and organize clips of information while conducting research online. The browser-based tool permitted a user to write notes, clip text and images, and save links from pages during a browser session.
Google Cloud Connect was a free cloud computing plug-in for Windows Microsoft Office 2003, 2007 and 2010 that can automatically store and synchronize any Microsoft Word document, PowerPoint presentation, or Excel spreadsheet to Google Docs in Google Docs or Microsoft Office formats. The Google Doc copy is automatically updated each time the ...
In July 2011, Google announced that it was discontinuing Google Labs. [3] Although many of the experiments have been discontinued, a few have moved to the main search pages or have been integrated into other products. Google still has many links to its defunct "Labs" tools in Google blogs that are readily accessible through a Google search.
Google claims TPU v5 is nearly twice as fast as TPU v4, [36] and based on that and the relative performance of TPU v4 over A100, some speculate TPU v5 as being as fast as or faster than an H100. [37] Similar to the v4i being a lighter-weight version of the v4, the fifth generation has a "cost-efficient" [38] version called v5e. [21]
DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. [1] It is designed to make ML models shareable, experiments reproducible, [2] and to track versions of models, data, and pipelines. [3] [4] [5] DVC works on top of Git repositories [6] and cloud storage. [7]
Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, [9] AWS Batch for offline batch processing, [9] [10] or Amazon Kinesis for real-time processing.