Training Tensorflow Models with Azure ABS Data

Azure ABS

Azure Data Lake Storage is primarily designed to work with Hadoop and all frameworks that use the Hadoop FileSystem as their data access layer (for example, Spark and Presto). It is a massively scalable, secure data lake functionality built on Azure Blob Storage which is designed for big data analytics and offers a hierarchical file system.

Tensorflow Models

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. TensorFlow helped launch the deep learning revolution: with pre-trained models, data, and high-level APIs, it has become easy for everyone to build ML and DL models. One of the important Tensorflow features is that it is flexible in its operating model, meaning it has modularity and the parts of it that you want to make standalone it offers you that option. TensorFlow.js is a JavaScript-based framework to run machine learning models within the browser. Any modern browser can run the TensorFlow model with no changes to the code.
With the growing popularity of both Azure ABS for storage and Tensorflow models for AI deployments, it is unsurprising that many organizations are seeking to train Tensorflow models using data in Azure ABS. Kaspian offers native connectors for this operation. Just register your Azure ABS datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy Tensorflow models using any data in your cloud with minimal setup or management.
Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.

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