In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.
Domino 5.3 Unleashes Hybrid and Multi-Cloud Data Science at Scale
Domino Data Lab, provider of a leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, announced the availability of Domino 5.3, a major update that improves the time-to-value and impact of data science at scale across any cloud or on-premises infrastructure. The latest platform version introduces a private preview of its Domino Nexus hybrid and multi-cloud capabilities, plus an expanded suite of connectors to simplify and democratize access to critical data sources, and new GPU inference capabilities that make it easier to productionize high value data science projects, including deep learning.
“Modern enterprise data science teams need access to a wide variety of data and infrastructure across different clouds, regions, on-premises clusters and databases,” said Nick Elprin, co-founder and CEO of Domino Data Lab. “Domino 5.3 gives our customers the ability to use the data and compute they need wherever it lives, so they can increase the speed and impact of data science without sacrificing security or cost efficiency.”
DataScalp Launches Consumer-Powered Business Performance Ranking Platform
DataScalp, the online platform that captures consumer experiences and uses consumer data to rank companies in a performance dashboard, announced the company’s official launch. Its proprietary business ranking platform enables consumers to report poor industry outcomes. The platform aggregates those consumer experiences, giving consumers a powerful voice and a tool to learn from others’ experiences.
“Consumers should be empowered to report the degree of performance of one company versus another, selecting and rewarding good performers versus avoiding and punishing poor performers. Nowhere is this required more than in the airline industry,” said Dwight Harris, Jr., founder and CEO of DataScalp. “Despite the availability of social listening, surveys, the Better Business Bureau and focus groups, businesses still struggle to understand the voice of the customer. DataScalp solves this issue by providing a revolution in consumer-driven data that provides the highest level of analysis and corporate response.”
Apollo GraphQL Introduces GraphOS, a Platform to Build, Connect and Scale Any Supergraph
Apollo GraphQL, the supergraph company, introduced Apollo GraphOS, the world’s first end-to-end platform to build, connect, and scale any supergraph. GraphOS is the execution fabric for the supergraph – a powerful runtime that connects backend and frontend systems in a modular way. It offers self-hosted or cloud-hosted routing, so users can choose to operate the supergraph in the cloud, and build without complex infrastructure setup or configuration.
“The supergraph is built to solve the needs of application developers – to give them the flexibility and resources they need to build amazing experiences without the complexity and friction that constantly gets in the way,” said Matt DeBergalis, Apollo’s CTO and co-founder. “We’ve seen industry consensus that the supergraph meets those needs – some of the world’s biggest and most forward-thinking companies are using it. With GraphOS, we’re removing the complexity involved with operating a supergraph, while giving everyone an easy onramp to experience the benefits of having one.”
ArangoDB Democratizes Machine Learning with ArangoGraphML
ArangoDB, the company behind the graph data and analytics platform, released two new products to make machine learning more accessible: ArangoGraphML (closed beta) and Jupyter Notebooks-as-a-service (open beta). ArangoGraphML provides enterprise-ready, graph-powered machine learning (ML) available as a cloud service – helping both experts and non-experts turn deeper insights into more powerful innovations. Jupyter Notebooks-as-a-service provide fast and secure data exploration for busy data scientists by keeping graph data in the cloud. Both of today’s releases are additional tools in ArangoDB’s already extensive ML toolset, which includes a healthy ecosystem of plug-and-play adapters for cuGraph, DGL, NetworkX, and PyG.
“Traditional machine learning misses connections and relationships between data points, which is where graph shines,” said Jörg Schad, Ph.D., ArangoDB CTO. “Graph machine learning is one of the most exciting trends in machine learning, but currently only accessible to large enterprises with dedicated teams of data scientists. ArangoDB wants to empower everyone to leverage graph technologies, including machine learning, to create business value. ArangoGraphML is another exciting step in this direction.”
Pega helps make it fast and simple to build RPA bots with its revamped low-code Robot Studio
Pegasystems Inc. (NASDAQ: PEGA), the low-code platform provider that builds agility into the world’s leading organizations, announced the latest release of Robot Studio, the robotic process automation (RPA) low-code authoring environment for Pega’s intelligent automation platform. The revamped Robot Studio helps make it even easier for users of any skill level to quickly build robotic automations that help make any business process more efficient.
“Many organizations start their digital transformation journey with RPA so they can streamline their operations and make work easier for their employees. But ironically, for many other solutions in the market, RPA isn’t always that easy,” said Eric Musser, general manager, intelligent automation, Pega. “With the newly revamped Robot Studio, users of all skill levels can build bots faster than ever before without sacrificing robustness so they can automate mundane tasks and focus on more meaningful work.”
John Snow Labs Announces Finance NLP and Legal NLP, Bringing State-of-the-Art Natural Language Processing to New Domains
John Snow Labs, the healthcare AI and NLP company and developer of the Spark NLP library, announced the launch of two new products: Finance NLP and Legal NLP. The two libraries come with a series of new pretrained models and state-of-the-art algorithms, able to carry out Entity Recognition, Relation Extraction, Assertion Status Detection, Entity Resolution, De-identification, Text Classification, and more. Spark NLP is used by 50% of practitioners in the finance industry, signaling a demand for a dedicated offering.
“The highly specific jargon and nuanced semantics in legal and financial documents, paired with the sheer amounts of text these industries generate present a massive opportunity for natural language processing to help automate, simplify, and optimize operations,” said David Talby, CTO, John Snow Labs. “Finance NLP and Legal NLP enable that by providing current state-of-the-art accuracy, a broad set of out-of-the-box models for common use cases, and ease of use building them into production systems.”
Bigeye launches a monitoring-as-code solution to simplify data observability at enterprise scale
Bigeye announced the availability of the Bigeye CLI (command-line interface) and Bigconfig, a highly ergonomic and enterprise-grade approach to expressing data monitoring as code. Bigconfig is the first monitoring-as-code solution to support enterprise-scale data observability. It was designed in partnership with data engineers who use Bigeye on hundreds to thousands of tables at a time. Through Bigconfig’s declarative format, dynamic data tagging, and reusable monitoring definitions, it is simple to seamlessly deploy monitoring on thousands of existing tables.
Bigeye CEO Kyle Kirwan says, “Bigconfig helps us give developers on data teams an unmatched set of tools for building data observability into their environments and workflows, no matter how large or complex.”
AMAX Launches LiquidMax™ LA-2 Liquid Cooled Data Science Workstation
AMAX, a leading provider of customized rack-scale High-Performance Computing (HPC) solutions and server appliance manufacturing, announced the new LiquidMax™ LA-2 workstation supporting the AMD 3rd Gen EPYC™ 7003 CPU series with AMD 3D V-Cache™ technology and up to 64 cores to its LiquidMax™ series of liquid-cooled workstations. The LA-2 boasts the capability to support up to seven NVIDIA A100 or A6000 GPUs for increased HPC power to complete high-density workloads faster. It is an intelligent GPU workstation for enterprise and research infrastructures that need to accelerate deep learning training and scientific visualization applications. With the introduction of the LA-2 by AMAX, researchers can work with large datasets at interactive speeds without dependency on expensive cloud platforms or competing resource allocation from on-premise clusters.
The ultra-quiet LiquidMax™ LA-2 provides the most powerful and eco-friendly platform in convenient workstation form, backed by a full warranty with global tech support for peace of mind. The single CPU LA-2 features a single root topology to maximize GPU to GPU bandwidth, up to 4TB of DDR4 system memory, and up to 176TB of raw storage across 8 hot-swappable WD Gold® Enterprise Class Hard Drives for maximum storage capacity. The user-friendly turnkey design showcases AMAX’s engineering expertise with a proprietary enterprise-grade liquid cooling system featuring custom patented CPU and GPU cold plates for more efficient thermal management and dual Gold® rated power supplies. The chassis includes durable casters for mobility and real-time LED display monitoring of key safety functions.
Artificial Intelligence detects cybersecurity threats and predicts attacks before they happen
Blacklight, the new artificial intelligence (AI) powered software from OwlGaze, can detect cybersecurity threats and predict attacks before they happen. Blacklight’s next-generation predictive AI threat detection offers British businesses and organisations a robust solution for real-time security detection and monitoring, ushering in a new cybersecurity paradigm. Blacklight is the first ever truly predictive, cloud-native, AI-powered cybersecurity threat detection software. It provides a centralized cybersecurity command center for any organization, and enables a proactive approach to identify, prioritize and prevent cyber-attacks using advanced correlation and AI. Blacklight’s real-time detection of cybersecurity threats, integrating multiple data points and using advanced pattern recognition, alerts security teams to threats faster and easier than ever before.
“In cybersecurity, in order to identify threats that you’ve never seen before, you must change how you are looking for threats,” explains Ralph Chammah, Chief Executive Officer of OwlGaze. “Rather than looking for what you think is an attack, examine everything that is not normal behaviour. This approach is what makes Blacklight genuinely different. Blacklight’s real-time detection of cybersecurity threats, integrating multiple data points and using advanced pattern recognition, means security teams are alerted to threats faster and easier than ever before.”
Section’s Distributed GraphQL Hosting Allows Organizations to Quickly Launch and Scale Location-Optimized, Multi-Cloud API Servers
Section, a leading cloud-native distributed compute provider, announced its new Distributed GraphQL Service, allowing organizations to quickly launch and easily scale location-optimized, multi-cloud API servers. Organizations can host GraphQL in datacenters across town or around the world to improve API performance and reliability, lower costs, decrease impact on back-end servers, and improve scalability, resilience, compliance, security and other factors – all without impacting their current cloud-native development process or tools. Section handles day-to-day server operations, as its clusterless platform automates orchestration of the GraphQL servers across a secure and reliable global infrastructure network.
“Distributing API servers and other compute resources makes all the sense in the world for developers, as long as it’s easy to do,” said Stewart McGrath, Section’s CEO. “Our new Distributed GraphQL service is simple to start, gives you immediate access to a global network, and automates orchestration so developers can simply focus on their application and business logic.”
cnvrg.io Metacloud provides AI launchpad to a New Intel Developer Cloud
cnvrg.io, an Intel Company and provider of Metacloud Platform as a Service for AI/ML, announced that the new Intel Developer Cloud is now available via the cnvrg.io Metacloud platform, providing a fully integrated software and hardware solution. With cnvrg.io Metacloud orchestration of workloads and cloud infrastructure, developers and AI specialists can leverage Intel Developer Cloud for all their application needs. AI computing is becoming increasingly complex and expensive. AI comes with high operational costs, and AI developers are unable to access specialized AI hardware for their AI/Data workloads. With cnvrg.io and Intel Developer Cloud, developers can instantly access the latest AI/Data optimized Intel resources seamlessly, improving performance and ROI of AI, data, and cloud infrastructure. Developers will get access to the latest Intel AI hardware, months before it is available in other cloud service providers.
“cnvrg.io, together with Intel Developer Cloud is excited to deliver an end-to-end solution of software and hardware that enables any AI developer to easily run AI workloads and with just one click,” says Yochay Ettun, CEO and Co-founder of cnvrg.io. “Now developers have on demand access to dedicated, optimized and hosted AI compute and hardware within the new Intel Cloud Services platform directly from cnvrg.io. It is another major milestone for cnvrg.io Metacloud.”
AtScale Announces Data Science and Enterprise AI Capabilities Within Semantic Layer Platform
AtScale, a leading provider of semantic layer solutions for modern business intelligence and data science teams, announced an expanded set of product capabilities for organizations working to accelerate the deployment and adoption of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s unique position within the data stack with support for common cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks.
“Despite rising investments, greater adoption of AI/ML within the modern enterprise is still hindered by complexity,” said Gaurav Rao, Executive Vice President and General Manager of AI/ML at AtScale. “The need for AI is huge, exploration is on the rise, but many businesses are still not able to use the predictive insights AI models can generate. Here at AtScale we can leverage our unique position in the data stack to streamline and simplify how the business can consume and use AI immediately, generating faster time to value from their enterprise AI investments.”
Arcion Announces Agentless Change Data Capture for Azure, SQL Server, Oracle, SAP HANA, and MySQL
Arcion, creator of the cloud-native, CDC-based data replication platform, announced agentless change data capture (CDC) for all of its supported databases and applications. Enterprises can now replicate data in real time, at scale, with guaranteed delivery — but without the inherent performance issues, security concerns, and administrative burdens associated with the installation of proprietary software on database servers. Arcion enables faster, more agile analytics and AI by replicating mission-critical transactional enterprise databases to cloud-based data platforms in real time, at scale, and with guaranteed transactional integrity. It is the only fully managed, distributed data replication-as-a-service on the market today that makes it possible to deploy zero-code, zero-maintenance pipelines in just minutes.
“Arcion’s agentless approach offers simplicity and peace of mind,” said Rajkumar Sen, chief technology officer at Arcion. “Providing maximum flexibility and agility, data engineering and IT teams can deploy streaming pipelines in minutes, without the typical planning cycles and dependencies often associated with agent-based solutions. With Arcion, data engineers can now deploy streaming replication pipelines without extensive database administrative changes and IT reviews and, at the same time, significantly reduce the risk of a security breach. They get scalable real-time data replication without the administrative nightmares and risks associated with running agent-based software on production cloud environments. For performance, flexibility, and total cost of ownership, agentless CDC wins every time.”
Label Studio 1.6 Release Adds Video Object Tracking and New Annotation UI to Popular Open-Source Data Labeling Platform
The Label Studio 1.6 open-source release now supports video object tracking in general availability, making it the most popular open-source data labeling platform to support all data types—video, image, text and hypertext, time-series and audio. In addition to the new video player that supports frame-by-frame video object tracking, the latest release also features a new annotation user interface (UI) that is more efficient, ergonomic and flexible. Label Studio is a popular open-source labeling software, with more than 5 million downloads, 150,000 users, 10,000 stars on GitHub, and a community of nearly 6,000 data science professionals. The platform was designed to be flexible and extensible—not only by supporting a wide range of machine learning and AI use cases—but by providing a programmable interface and webhooks, Python SDK, integrations with all major cloud storage providers, and customizable workflows and labeling interfaces for each project.
“For the first time, data science professionals have a single, open-source data labeling platform and workflow for all of their projects, regardless of data type, without having to cobble together and manage different tools,” said Michael Malyuk, CEO and co-founder of Heartex. “Label Studio 1.6 is an important move toward a future in which we think about dataset development as a pipeline workflow, making the work of these professionals more productive, streamlined and extensible.”
Vyasa Adds Real-Time Dashboards to Layar Data Fabric
Vyasa, an innovative provider of highly scalable deep learning A.I. analytics software for healthcare, life sciences and business applications, introduced its latest application interface, Signal. Featuring an intuitive design, Signal enables users to monitor trends and identify anomalies in their Layar data fabric through highly-visual charts and graphs delivered in a single dashboard.
“Today’s organizations face significant data accessibility challenges – from content being stored across multiple silos to managing various structured and unstructured file formats to outdated processes for collecting and analyzing data that are time and resource intensive,” said Vyasa Founder & CEO, Dr. Christopher Bouton. “At Vyasa we’re developing cutting-edge deep learning technologies to address this issue and Signal is an excellent example of that. Now users can quickly access the insights they need in charts and graphs which are familiar and easy to analyze without having to worry about finding the data or having the technical acumen to develop a code or query to collect the information they’re seeking.”
Materialize Makes Using Real-Time Data As Simple As Batch with New Distributed Streaming Database
Materialize announced early availability of its distributed streaming database, which enables immediate, widespread adoption of real-time data for applications, business functions, and other data products. Industry-firsts in streaming data announced today include separation of storage and compute, strict-serializability, active replication, horizontal scalability, and workload isolation — all through a simple SQL interface available as a fully-managed cloud service. Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features.
“By abstracting away the tedious stream processing work and allowing both data and software engineers to focus on logic in SQL, we help them create customizable, powerful data experiences, quickly, easily, and cost-effectively,” said Materialize Co-founder and Chief Scientist Frank McSherry. “Real-time products haven’t been impossible to implement, they’ve just been extremely difficult, due to the need for custom development and ongoing maintenance. Standard SQL significantly lowers the bar to engagement and should be sufficient for all but the most complex use cases, enabling valuable engineering resources to be applied to the most sophisticated challenges.”
StormForge Enables the Industry’s First Bi-Dimensional Kubernetes Pod Autoscaling Capability, Ensuring Cost Efficiency Without Sacrificing Performance
StormForge, a leader in cloud-native application performance testing and resource optimization, announced its StormForge Optimize Live solution now enables the industry’s first bi-dimensional Kubernetes pod autoscaling. The enhanced capabilities are available now. StormForge Optimize Live uses machine learning to automatically right-size pods while also setting a desired target utilization for the horizontal pod autoscaler (HPA). This enables vertical and horizontal autoscaling to work together without contention, to minimize resource usage and cost without sacrificing application performance or reliability.
“The promise of Kubernetes is still beyond the reach of so many organizations, but bi-dimensional pod autoscaling, now made possible by StormForge Optimize Live, will change that for the vast majority of Kubernetes users,” said Matt Provo, CEO at StormForge. “We’re excited to offer yet another industry-first innovation from StormForge, which we expect will unleash innovation in enterprises across the globe through application performance improvements and cloud cost savings.”
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