Native TensorFlow Integration Enables Seamless Deep Learning on Data in GridGain
GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite™, announced the immediate availability of GridGain Professional Edition 2.7, a fully supported version of Apache Ignite 2.7. GridGain Professional Edition 2.7 introduces TensorFlow integration for enhanced training of deep learning (DL) models. GridGain Professional Edition 2.7 also provides enhanced usability, including expanded support for thin clients, as well as Transparent Data Encryption at rest to improve security. Together, the features make it easier to use the GridGain In-Memory Computing Platform for more use cases, such as for achieving the speed and scalability required for implementing real-time continuous learning for digital transformation and omnichannel customer experience initiatives.
TensorFlow Integration for High Performance Deep Learning Model Training
TensorFlow, a popular open source deep learning framework, is a software library for high performance numerical computation. Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs) and devices.
The GridGain integration with TensorFlow allows GridGain users to easily share data stored in GridGain with TensorFlow. Functioning as an in-memory data source for TensorFlow, GridGain allows users to leverage the TensorFlow deep learning framework for real-time deep learning model training without requiring a dedicated data store for TensorFlow.
GridGain Professional Edition 2.7 also includes new preprocessing APIs and additional machine learning (ML) algorithms. These algorithms make it easier for organizations to apply the power of GridGain in-memory computing capabilities to more ML use cases such as credit card fraud detection, mortgage approvals, or ecommerce product recommendations.
Expanded Programming Languages Support
GridGain Professional Edition 2.7 now offers expanded support for thin clients with support for new languages, including Python, Node.JS, PHP, C++, .NET and Java. Support for these thin clients makes it easier for users accustomed to programming in one of these languages to use their preferred language when coding for GridGain.
Transparent Data Encryption at Rest
GridGain Professional Edition 2.7 includes Transparent Data Encryption at rest. Encryption is applied to the data stored in the GridGain Persistent Store, so even if a cybercriminal were to breach a GridGain cluster, they could not see the data in plain text.
“The latest release of GridGain Professional Edition makes it easier to leverage deep learning via TensorFlow. GridGain is also now easier to implement and secure, allowing users to leverage in-memory computing for the high performance and massive scalability they need to deliver on their digital transformation and omnichannel customer experience initiatives,” said Terry Erisman, Vice President of Marketing for GridGain Systems.