Training Tensorflow Models with data in your Data Lake

Data Lake

Data lakes are popular because they provide a consolidated, centralized storage area for raw, unstructured, semi-structured, and structured data taken from multiple sources and lacking a predefined schema. They specialize in ingesting structured, semi-structured and unstructured data and provide mechanisms to easily ingest streaming data in addition to batch loads. Data lakes are open format so users avoid lock-in to a proprietary system like a data warehouse. They are also highly durable and low cost because of their ability to scale and leverage object storage.

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 data lakes for storage and Tensorflow models for AI deployments, it is unsurprising that many organizations are seeking to train Tensorflow models using data in their data lake. Kaspian offers native connectors for the most popular data lakes. Just register your data lake as a 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.