Training Tensorflow Models with PostgreSQL Data

PostgreSQL

PostgreSQL (a.k.a. Postgres) is a free and open-source relational database management system that is widely used by developers. It is highly customizable and has many useful features for developers. PostgreSQL is one of the most widely-used free and open-source relational database management systems that focus primarily on SQL compliance and extensibility. PostgreSQL offers true community-driven development, extensibility, strong SQL compliance, and useful features such as table inheritance and function overloading.

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 PostgreSQL for storage and Tensorflow models for AI deployments, it is unsurprising that many organizations are seeking to train Tensorflow models using data in PostgreSQL. Kaspian offers native connectors for this operation. Just register your PostgreSQL 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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