Month: June 2020 (Page 4 of 7)

DataStax announced the private beta of Vector, an AIOps service for Apache Cassandra. Vector continually assesses the behavior of a Cassandra cluster to provide developers and operators with automated diagnostics and advice, helping them be consistently successful with Cassandra and DataStax Enterprise (DSE) clusters. Vector provides recommendations with detailed background knowledge and offers multiple ways to fix a problem. With this embedded knowledge base, Vector is able to analyze individual nodes, compare behavior to other nodes in the cluster, and serve up recommendations, such as: Cassandra and operating system configuration, schema design, and Cassandra performance and query techniques. Vector features:

• Automated expert advice – Proactively identifies current and potential issues to help developers and operators solve problems quickly. Automated advice provides contextual learning with background knowledge to build skills.
• Continuous updates – Rules and advice are continuously updated, deployed to SaaS and on-premises applications, and automatically applied to clusters.
• Hands-off management – Advanced visualizations of system usage with insightful charting to understand tables, keyspaces, and nodes. Vector helps developers and operators see and understand how the cluster is performing and its configuration without having to log into Cassandra nodes.
• Cassandra skills development – Helps strengthen Cassandra skills and knowledge by providing detailed advice and recommendations. Vector helps to reduce unexpected and unplanned items.

https://www.datastax.com/

ABBYY announced the launch of NeoML, an open-source library for building, training, and deploying machine learning models. Available now on GitHub, NeoML supports both deep learning and traditional machine learning algorithms. The cross-platform framework is optimized for applications that run in cloud environments, on desktop and mobile devices. Compared to a popular open-source library (according to internal tests) NeoML offers 15-20% faster performance for pre-trained image processing models. Developers can use NeoML to build, train, and deploy models for object identification, classification, semantic segmentation, verification, and predictive modeling.

NeoML is designed as a universal tool to process and analyze data in a variety of formats including text, image, video, and others. It supports ​​C++, Java, and Objective-C programming languages; Python will be added shortly. NeoML’s neural network models support over 100 layer types. It also offers 20+ traditional ML algorithms such as classification, regression, and clustering frameworks. The library is cross-platform – a single code base that can be run on popular operating systems including Windows, Linux, macOS, iOS, and Android – and optimized for both CPU and GPU processors.

NeoML supports the Open Neural Network Exchange (ONNX), a global open ecosystem for interoperable ML models. The ONNX standard is supported jointly by Microsoft, Facebook, and other partners as an open source project. ABBYY invites developers, data scientists, and business analysts to use and contribute to NeoML on GitHub, where its code is licensed under the Apache License 2.0. The company offers developer support, ongoing review of reports, regular updates, and performance enhancements.

https://github.com/neoml-lib, https://www.abbyy.com/

Microsoft Corp. and SAS announced an extensive technology and go-to-market strategic partnership. The two companies will enable customers to run their SAS workloads in the cloud, and will migrate SAS’ analytical products and industry solutions onto Microsoft Azure as the preferred cloud provider for the SAS Cloud. SAS’ industry solutions and expertise will also bring value to Microsoft’s customers across health care, financial services and many other industries. This will include optimizing SAS Viya, the latest release of the company’s cloud-native offering, for Azure as well as integrating SAS’ deep portfolio of industry solutions into the Azure Marketplace. Additionally, Microsoft and SAS will explore opportunities to integrate SAS analytics capabilities, including industry-specific models, within Azure and Dynamics 365 and build new market-ready joint solutions for customers that are natively integrated with SAS services across multiple vertical industries. Microsoft and SAS are already supporting customers with solutions that help them capitalize on the vast amount of data being generated by the Internet of Things by combining Microsoft’s Azure IoT platform with SAS’ edge-to-cloud IoT analytics and AI capabilities. Additional SAS products and solutions will begin rolling out later this year.

https://www.sas.com/, https://news.microsoft.com