Kibernetika is accelerating adoption and enabling development of AI applications in the enterprise, with a particular focus on deep learning. The Kibernetika Machine Teaching solution is designed for end-to-end lifecycle management of building, training and deploying large scale artificial intelligence applications.

Artificial intelligence technologies are being employed for an increasing variety of use cases across consumer, enterprise, and government markets around the world. Tractica forecasts that the revenue generated from the direct and indirect application of AI software will grow to $59.75 billion by 2025.

Enterprise companies are experiencing a dramatic change in the way applications are built, deployed and managed. New AI frameworks and libraries are coming out every day. There is a growing variety of target HW platforms to run the AI algorithms on. The result is a significant shortage of data science and engineering talent required for the development of AI applications and considerable challenges building those applications with new software and hardware and then deploying them into production in a stable and reliable manner

The Kibernetika Machine Teaching solution delivers an integrated suite of enterprise-grade capabilities for machine learning and deep neural networks, providing the customer with an agile, elastic solution for transforming their business. It dramatically reduces the efforts required to manage the AI application lifecycle in production and lets both data scientists and IT specialists focus on other mission-critical tasks.

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Kibernetika integrated Intel AI technology into our Machine Teaching. Empasys of Intel AI tech on inference and production works very well with Kibernetika Continuous Production.

Following Intel AI technologies are supported by Kibernetika Machine Teaching for implementation of customer end-to-end AI/ML development and deployment. And if needed our expert team is available to complete the entire project or augment an existing team.

Intel OpenVINO toolkit

OpenVINO toolkit extends workloads across Intel@ CPU and accelerated hardware and maximizes performance. Kibernetika integrated toolkit with serving and deployment facility, giving users the ability to optimize and deploy models to target platforms of Intel ecosystem from high-performance data centers, to edge devices.

Tensorflow with MKL-DNN

TensorFlow* is a machine learning framework, demanding efficient utilization of computational resources. In order to take full advantage of Intel® architecture and to extract maximum performance, Kibernetika provides the TensorFlow framework that has been optimized using Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) primitives.

Spark and BigDL

BigDL is a distributed deep learning library for Apache Spark*. Kibernetika solution is integrated with Spark and provides BigDL development environments that can run on top of existing Spark or Apache Hadoop clusters.

Intel Movidius SDK

The Intel® Movidius™ Neural Compute SDK includes a set of software tools to compile, profile, and check (validate) DNNs developed to run on Intel Myriad VPU embedded processor. Kibernetika solution provides Movidius SDK development environments and access to Movidius hardware from the cloud.

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