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.
Synthesis AI Launches HumanAPI to Create Millions of Photorealistic Digital Humans, On-Demand
Synthesis AI, a pioneer in synthetic data technologies, released HumanAPI, a significant expansion of the company’s synthetic data capabilities enabling the programmatic generation of millions of unique, high-quality 3D digital humans. This announcement comes months after the launch of the FaceAPI synthetic data-as-a-service product, which has delivered over 10M labeled facial images for leading smartphone, teleconferencing, automobile, and technology companies. HumanAPI is the next step in the company’s journey to support advanced computer vision Artificial Intelligence (AI) applications.
“The ability to obtain accurate 3D labeled human data on-demand will fundamentally change the development of more sophisticated human AI models,” said Andrew Rabinovich, PhD and Headroom co-founder and CTO and former Head of AI for Magic Leap. “This is an important development in expanding the use of synthetic data across multiple use-cases and into new emerging technologies.”
DDN Advances Powerful Enterprise AI Breakthroughs with Its Latest AI Storage Platform
DDN®, a leader in artificial intelligence (AI) and multicloud data management solutions, announced the latest addition to its powerful A3I® solutions, the AI400X2, which delivers twice the performance of the previous generation appliance in the same footprint, furthering A3I performance leadership among AI data systems. Additional performance and usability enhancements introduced with the AI400X2 include Hot Node integration and a streamlined intelligent user interface to accelerate and simplify enterprise IT operations. The AI400X2 joins the lineup of A3I appliances that are deployed today and powering thousands of NVIDIA DGX™ systems globally across a wide range of production environments in financial services, life sciences, healthcare, and autonomous vehicle industries. The enhanced speed, efficiency and intelligence of the AI400X2 moves organizations toward an increasingly hands-off approach to AI application management that improves competitiveness and overall customer experience without sacrificing security.
“We designed this next generation of A3I solutions to give customers the most efficient, scalable and reliable AI data platform,” said Dr. James Coomer, senior VP of products, DDN. “Customers continue to turn to DDN to remove the complexity from their AI initiatives. AI is a powerful business enabler, and the AI400X2 will help customers get into production faster and reduce time to results.”
Run:AI releases dynamic scheduling for NVIDIA Multi-Instance GPU technology
Run:AI, a leader in compute orchestration for AI workloads, announced dynamic scheduling support for customers using the NVIDIA Multi-Instance GPU (MIG) technology, which is available on NVIDIA A100 Tensor Core GPUs as well as other NVIDIA Ampere architecture GPUs. Run:AI now enables the creation and management of MIG instances on the fly, which is particularly useful for smaller inference and light training workloads that don’t require an entire GPU. With Run:AI, MIG-enabled GPUs can be configured automatically, according to demand. For example, when a user requests access to one-seventh of a GPU, one MIG partition is configured and provided to the user. This process is seamless for the researcher requesting resources.
“NVIDIA MIG technology is revolutionary for running multiple simultaneous jobs like inference on one GPU,” said Omri Geller, Run:AI’s co-founder and CEO. “Now, with Run:AI’s dynamic scheduling for MIG, researchers simply request resources, and the platform automatically makes use of the right amount of compute for the job, adding flexibility and speed, and reducing idle GPU time.”
Instaclustr Open Sources Shotover, a High Performance L7 Data-Layer Proxy for Improving Database Operations and Interoperability
Instaclustr, which helps organizations deliver applications at scale by managing their open source data infrastructure, announced that the Shotover proxy project is now available as fully open source software. Shotover is a high performance L7 data-layer proxy for controlling, managing, and modifying the flow of database requests in transit. The open source community and others interested can find the latest release of Shotover from GitHub.
“We define our business at Instaclustr by our commitment to providing powerful open source technologies in their pure open source form – free of open core or proprietary lock-in,” said Ben Bromhead, Chief Technology Officer at Instaclustr. “With Shotover, we’re honored to share an open source project with a community we know will put it to good use and help it to grow. We eagerly invite anyone interested to explore, try, and contribute to Shotover.”
ScaleFlux Announces Next Generation Portfolio, Bringing Highly Efficient Computational Storage to the Masses
ScaleFlux, Inc., a leader in deploying Computational Storage at scale, announced a new suite of products based on its new chip, ScaleFlux SFX 3000 storage processing units. This also includes Computational Storage optimized software (“CSware”) to make it easier for customers to adopt Computational Storage across a wider range of use cases.
“Organizations are challenged by the deluge of data that needs to be stored, transferred, and processed to support the data-centric economy. Computational Storage is the solution, helping alleviate compute and memory bottlenecks, reducing data movement, and improving the ROI of data center and IT infrastructure,” said Hao Zhong, Co-founder and CEO of ScaleFlux. “With this new suite of products, we are making it easier than ever for organizations to adopt and realize the benefit of Computational Storage at scale.”
Noogata Launches AI Location Analytics Library for Bricks & Mortar Insights
Noogata, a global leader in no-code artificial intelligence (AI) data analytics for enterprises, announced the launch of its location analytics library. Building on the success of its existing ecommerce library, the location analytics library applies the power of Noogata’s no code AI data analytics platform to physical locations for consumer packaged goods (CPG) brands. This allows sales and marketing leaders to leverage AI to gain actionable insights, including generating and scoring leads for new sales opportunities, through understanding the locations that are relevant for their business. Until now, sales and marketing teams struggled to enrich location data with all the relevant information from hundreds of external data sources, such as demographics, economic and sales trends and to apply the advanced analytics techniques required to gain real insights. Noogata’s location analytics library automates this entire process and generates a unique “location fingerprint” based on thousands of different features for each location.
“Enterprise location analytics efforts have been notoriously underwhelming to date, often resulting in anecdotes and typically failing to drive business impact at scale. With our location analytics library we help companies take the next step to harness data that is extremely difficult to tap into in a focused way and deliver rapid business impact,” said Assaf Egozi, CEO and co-founder, Noogata. “This impact goes beyond supporting bricks and mortar sales and marketing. It also boosts ecommerce sales and marketing efforts with omnichannel insights. And further, it has been shown that a critical success factor for smaller brands driving ecommerce sales is the targeted optimization of their bricks and mortar footprint.”
BigPanda Accelerates AIOps Adoption With New Integrations and Self-Service APIs
BigPanda, Inc., a leader in Event Correlation and Automation powered by AIOps, announced a suite of new and updated integrations and self-service APIs designed to accelerate AIOps adoption across fragmented teams and tools. Specifically, native integrations with Jira and AppDynamics deliver a new framework for collaboration and communication, while a trio of self-service APIs enables organizations to scale AIOps deployments with ease.
“Automating repeated tasks and workflows is key to accelerating incident response and allowing IT Ops to keep up with the pace of change and innovation that DevOps and SRE teams need to thrive,” said Elik Eizenberg, co-founder and CTO at BigPanda. “Our latest innovations give our customers the ability to embrace the rapid innovation of products and services while adhering to the same level of quality, reliability and serviceability that their end customers have come to expect.”
Era Software Announces EraCloud for Managing Petabytes of Log Data in Real-Time
Era Software, the company that helps enterprises observe cloud services and infrastructures, announced the general availability of EraCloud, a software as a service (SaaS)-based offering that includes the EraSearch observability and analytics platform optimized for real-time, low-cost log management. In addition to EraSearch, EraCloud features administrative functions that help organizations move from logs to insights in seconds, without operational complexity.
“Our vision for EraCloud is to provide organizations with a one-stop solution for all its logging needs,” says Todd Persen, co-founder and chief executive officer of Era Software. “As EraCloud evolves, modern IT teams will be able to ingest log data in any format, eliminating the need for pre-processing data or building and managing pipeline stages. It will help remove ingest delays, while continuing to make data available in real time for searching. Our goal is to help IT teams remove operational burden and empower them to focus on contributing to their core business.”
Conversica Unveils Conversational Account-Based Marketing Solution to Deliver Personalized Engagement at Scale
Conversica, Inc., a leader in Conversational AI solutions for enterprise revenue teams that help organizations attract, acquire and grow customers at scale, announced new Conversational Account-Based Marketing (ABM) capabilities available through its AI Assistants. Conversica’s Conversational ABM solution combines the value of personalized human dialog with the prompt, persistent, and consistent engagement of Conversational AI, enabling sales and marketing teams to execute at scale with increased engagement, accelerated deal cycles, and better customer experience for every account.
“The successful execution of an ABM strategy is dependent on access to insights and the use of those insights in highly personalized, human-like engagement,” said Sonny Dasgupta, Head of Product Marketing at Conversica. “We are excited to enter the Conversational ABM space with a solution optimized to do just that and more, including improving the quality of engagement, qualification speed, and conversation rates. As a result, organizations will be able to achieve personalization at scale and greater ABM effectiveness by expanding the number of targeted accounts, achieving better ROI, and having more predictable revenue through higher volume and greater sized deals.”
PredictHQ Demand Impact Pattern Makes Severe Weather Events Consumable and Explainable for Demand Forecasting
PredictHQ announced its Demand Impact Pattern for severe weather events, with data sets to help businesses prepare for major weather events and mitigate overall impact by integrating into machine learning models for demand forecasting. Research shows that abnormal weather disrupts the operating and financial performance of 70% of businesses worldwide. Every year, weather variability is estimated to cost $630 billion for the U.S. alone. Built specifically for the retail industry, PredictHQ’s Demand Impact Pattern gives data scientists a way to improve their forecast accuracy by incorporating weather event data into machine learning models, providing customers visibility into the leading, day-of and lagging effects of severe weather events.
“You can’t control severe weather but you can control your staffing and stocking to minimize impact and save millions each year,” said Campbell Brown, CEO at PredictHQ. “Previously, data scientists haven’t had the ability to make rapid, accurate updates to demand forecasts and plans, because there has been no intelligence layer making sense of severe weather events — we’ve changed that today. In the age of climate change, all businesses are seeking to get better at managing this impact as it rapidly escalates, and this is the first set of tools to make severe weather data fit-for-purpose in retail demand forecasting updates.”
Query Builder Removes PromQL’s High Barrier of Entry, Enabling Engineers to Spend More Time Driving Value for Their Organizations
Chronosphere, provider of a leading observability platform for cloud-native, released Query Builder, a way to quickly and simply chart Prometheus data with Prometheus Query Language (PromQL). This capability is available to all Chronosphere customers. Prometheus is the open source standard for metrics monitoring. The standard for Prometheus real-time querying and aggregation is PromQL but it is a challenging query language for engineers to fully understand and utilize. PromQL has specific query structures and syntaxes that users must learn and follow. Additionally, users have to incorporate variables unique to their organization such as metric name and dynamic filters based on label values. With so many possible metric types and variations to choose from, learning PromQL has become a burden for many engineers. Query Builder helps alleviate this burden, enabling engineers to spend less time crafting and troubleshooting their PromQL queries and more time on driving high-value work for their teams.
“We’ve experienced firsthand the steep learning curve of PromQL for engineers who are new to the open source standard, and our customers have also shared this pain with us. We’re solving this problem with our new PromQL query building experience which both removes the high barrier to entry for new users while helping existing users better understand and optimize their queries,” said Martin Mao, co-founder and CEO of Chronosphere.
McObject Announces Availability of eXtremeDB/rt for FreeRTOS
McObject®, an innovative pioneer in embedded database systems, announced the immediate availability of McObject’s revolutionary eXtremeDB®/rt database management system (DBMS) for FreeRTOS-based real-time embedded systems. eXtremeDB/rt is the first hard-real-time database system for mission- and safety-critical real-time systems. Real-time, in this context, means that eXtremeDB/rt enforces transaction deadlines and thereby maintains temporal consistency of the data while maintaining internal consistency through the well-known ACID properties. eXtremeDB/rt is built on the foundation and solid reputation of eXtremeDB, the first in-memory embedded database system written explicitly for embedded systems, released in 2001 and now found in over 30,000,000 deployments worldwide.
“From the beginning, we envisioned a hard real-time version of eXtremeDB and engineered it to be able to support this capability. We’ve watched while the real-time systems market has evolved along the same trajectory as the embedded systems that inspired us to create eXtremeDB: The systems need to manage more, and more complex, data” said Andrei Gorine, CTO of McObject. “This is being borne out in sophisticated systems such as autonomous air, ground and space vehicles, positive train control, power distribution/energy management systems and more. As the most-adopted real-time operating system, availability of eXtremeDB/rt on FreeRTOS is a top priority.”
Rivery Launches ‘Self-Service’ Platform, bringing autonomy and agility to every data team
Rivery, a leading end-to-end DataOps company, announced the launch of their ‘Self-Service’ Platform, enabling data analysts and data engineers to adopt their technology without the customary steps that make the purchasing process long and complex. The new ‘self-service’ offering allows users to access their platform to ingest, transform, and manage their data autonomously – even create data workflows using pre-engineered data models or “Kits”. Today, most data analysts rely on R&D teams to assist in setting up data pipelines and workflows. Rivery’s self-service alternative for individuals and teams will enable fully contactless capabilities and the future of DataOps integration.
“Rivery’s Self-Service platform is built for businesses and enterprises that are interested in managing and capitalizing on their data – quickly,” said Itamar Ben Hemo, co-founder and CEO of Rivery. “Our latest rollout means anyone can engineer a data stack. In my eyes, that’s a breakthrough for companies with limited capacity to make the most out of their data.”
Moogsoft Provides Unmatched Unified Cloud Monitoring Solution Through New Features and Integrations
Moogsoft, the AIOps pioneer and Observability leader, announced new product features and enhancements that increase context and improve workflow automation to provide customers with an unparalleled unified cloud monitoring solution. New enhancements and integrations include troubleshooting capabilities with Datadog for events and metrics; integration with Prometheus Alert manager to gain context and turn data into action; enhanced Landing Pages solution designed to answer user questions about features and configurations; and updates to reduce false positives and increase incident insights through flexible, transparent and preconfigured Splunk, Telegraf and AppDynamics integrations. Moogsoft’s unified cloud monitoring platform is the only solution that gives users the ability to view their entire tech stack in one dashboard while receiving automated, actionable insights to assist SREs and IT Operations teams in resolving issues quickly and efficiently.
“Monitoring alone can’t move businesses forward if they don’t understand the context of what went wrong. Context achieved through Observability helps customers make sense of data and illustrates how to prevent issues from happening again,” said Adam Frank, Moogsoft’s vice president of product management and UX design. “Moogsoft is committed to providing users with the only cohesive platform and holistic view of their systems. With the new enhancements and integrations, users can gain deeper insights from data, improve workflows and get back to what they do best — providing a top customer experience.”
Ondat Launches First SaaS Platform for Developer and DevOps Teams to Build and Run Stateful Kubernetes Applications
Ondat, a leading Kubernetes-native storage platform provider, announced the beta launch of a new, first-of-its-kind SaaS platform to allow customers to easily deploy and manage stateful Kubernetes applications with persistent data volumes. The Ondat SaaS platform provides operators with a holistic view of their Kubernetes and data resources enabling them to manage those from one place. In addition, now it is possible for customers to deploy their own database-as-a-service (DBaaS) to their users.
“The SaaS platform allows teams to deploy consistent production-ready data services while preventing lock-in to legacy storage vendors and cloud providers,” said James Brown, head of product and platforms at Ondat. “This adds enterprise reliability and improves performance, while reducing complexity and cost.”
Bigeye Releases Deltas to Automatically Compare and Validate Datasets in Seconds
Bigeye, the creator of a leading data observability platform, announced the release of Deltas, an industry-first solution that automatically compares and validates multiple versions of any dataset in seconds. Whether replicating data into a data warehouse, migrating from one cloud to another, or getting ready to promote data from staging to production, Deltas provides greater reliability in a fraction of the time. When moving data, all sorts of issues can occur, including delayed ingestion, dropped or duplicated records, and mutated values. These issues impact data quality, slow down projects, and erode trust. Comparing datasets is a crucial step for many data engineering projects, but it’s often difficult and time-consuming due to the need for custom SQL queries, complex and brittle spreadsheets, or bespoke Python scripts. Deltas extends Bigeye’s best-in-class data observability platform, making it easy to map a source and target, intelligently apply data quality metrics, and detect drift and discrepancies fast.
“We architected Bigeye to be an extensible framework, which allows us to apply data observability to all kinds of exciting use cases. We started by enabling data teams to automatically detect data quality and data pipeline issues. Now with Deltas, customers can easily compare and validate datasets,” said Egor Gryaznov, CTO and co-founder at Bigeye. “We look forward to enabling more groundbreaking user workflows through data observability in the near future.”
Introducing Neo4j AuraDB Free
Neo4jⓇ, a leading graph data platform, announced the general availability of Neo4j AuraDB Free™, a perpetual free tier of the company’s popular graph database as a service, AuraDB, with no credit card required to get started. With the release of AuraDB Free, Neo4j aims to bring the most deployed graph database platform to all, with zero friction and zero cost, accelerating the adoption of their transformative graph technology for modern intelligent applications. Developers can rapidly learn, prototype, and develop with graph technology without the burden of infrastructure management. The free tier delivers the fastest path to graph in an easy-to-use, fully managed cloud service.
“Neo4j AuraDB Free is an always-free tier of our database service, not merely a free trial,” explained Neo4j CEO and Co-Founder Emil Eifrem. “Some of the most innovative applications of Neo4j have come from our community, and we’re hoping AuraDB Free will further empower them and reduce friction to accelerate these ‘aha’ moments. Today, we’re stepping up to offer graph developers the easiest way to learn, test, and grow with us in the cloud.”
Innodata’s SaaS Data Annotation Platform Now Generally Available
Innodata Inc. (NASDAQ:INOD), a leading data engineering company, announced general availability of its Innodata Data Annotation Platform, a web-based, SaaS platform designed to reduce the cost of AI/ML projects while enabling users to develop more accurate models.
“At Innodata, we constantly listen to real-world pain points for inspiration and insight on how to innovate,” said Rahul Singhal, Chief Product Officer of Innodata. “After conducting extensive research, we found data scientists were seeking easy-to-use workflow processes, KPIs, and automated annotation capabilities not available in other data annotation products on the market. We built our Annotation Platform to directly address these needs, enabling data scientists to increase focus on model development and, ultimately, accelerate AI production rate.”
TrustLogix Launches Data Security Governance Platform; Secures All Data Across Any Cloud
TrustLogix, a Norwest Venture Partners portfolio company, announced its Data Security Governance Platform, a proxyless, cloud-native platform to unify data security, privacy and compliance without sacrificing performance. As enterprises further embrace digital transformation initiatives, the challenge of managing and securing data becomes more complex. TrustLogix Data Security Governance Platform was created to help data scientists and engineers modernize their data infrastructure in the cloud, making data at once accessible by the right users and completely secure and compliant.
“Enterprise data sharing and protection challenges are not going to go away,” Ganesh Kirti, Founder and CEO, TrustLogix. “From chaotic dependencies to complex data security to lack of usage visibility, modern enterprise data concerns are becoming more complex. Enterprises must secure their data while meeting privacy and compliance requirements and can’t be bogged down in the process. We created the TrustLogix Data Security Platform – to give enterprises a way to democratize data security and accelerate business innovation without sacrificing performance.”
Apollo GraphQL Introduces Federation 2 to Get More Organizations to the Graph
Apollo GraphQL, a pioneer in the use of open source and commercial GraphQL API technologies, announced the latest version of Apollo Federation, an open architecture that is designed to help organizations implement and orchestrate GraphQL services at scale. With the introduction of Federation 2, now in alpha, more organizations, small and large, can use Federation to unify their services into a single graph to deliver apps faster and streamline multi-team collaboration. Powered by a unified graph, Federation enables companies to gain competitive advantage by delivering new application experiences at a cadence that meets increasing customer demand. Federation 2 is simpler to use for small teams and more powerful for large teams. Organizations that tap into Federation early in their graph journey set themselves up for greater graph success–and strategic growth–by enabling modularization of the unified graph. These subgraphs can be owned and delivered independently, in parallel across multiple teams.
“Federation is the key to rapidly scaling your graph and your business,” said Matt DeBergalis, co-founder and CTO of Apollo GraphQL. “With these new capabilities, Apollo improves Federation’s ability to scale and evolve the graph fluidly as an organization’s apps, graph, and teams grow. With more than 1 million downloads each week, Apollo Federation is both the most popular solution for managing a distributed graph and the only true enterprise-grade solution for creating a unified graph.”
MicroAI™ Launchpad Accelerates Development of Smart Systems with Breakthrough Edge-Native AI
MicroAI™, a pioneer in edge-native artificial intelligence (AI) and machine learning (ML) products, announced MicroAI Launchpad™, a quick start development and deployment tool. Launchpad helps organizations simplify and accelerate the design, development, testing, and deployment of next-generation smart systems, that run embedded MicroAI software on microcontrollers (MCUs) and microprocessors (MPUs) in edge and endpoint devices. Launchpad makes it simple to handle customers with SIMs around the world and provides a flexible way to manage and reconfigure device profiles. Launchpad gives engineers a single pane of glass for customizable dashboards, including account creation, authentication, mobile SIM or LoRaWAN connectivity activation, credit card billing for global SIM connectivity, and easy onboarding of MicroAI’s embedded software libraries.
“MicroAI’s goal is to democratize the development of smart machines for all organizations across any industry,” said MicroAI CEO Yasser Khan. “Regardless of industry or product, building a next-generation smart device includes creating an edge AI model, but also integrating connectivity and cloud resources, as well as device activation and management.”
Monte Carlo, the Data Observability Leader, Launches Insights to Help Data Teams Understand What Data Matters Most to Their Business
Monte Carlo, the data reliability company, announced Insights, a new capability that helps companies understand which data is most important for the business, and in turn increase data trust. Built on top of the Monte Carlo Data Observability Platform, Insights leverages machine learning for monitoring and ranks events and assets based on their usage, relevance, and relationship to other tables and assets. Insights is the first solution to offer customers operational analytics about their data platform. With Insights, customers can measure and optimize the reliability, performance, cost and effectiveness of their data initiatives.
“Monte Carlo’s mission is to accelerate the adoption of data by eliminating data downtime – in other words, giving data teams the tools necessary to trust their data. Insights puts the metadata Monte Carlo generates in the hands of data engineers to help them answer the most important questions around how their efforts ultimately lead to higher quality data.” said Lior Gavish, CTO and co-founder, Monte Carlo. “Finally, businesses can get a holistic, end-to-end view of data health and utilization across the business.”
Streamlit Launches Streamlit Cloud That Transforms How Data Scientists Share Data
Streamlit, the creators of the fastest and most powerful app framework for machine learning and data science, today announced that Streamlit Cloud, formerly known as Streamlit for Teams, is available. Streamlit Cloud enables data scientists to instantly deploy and share apps with teammates, clients and other stakeholders so they can make rapid, data-informed decisions together.
“We started developing Streamlit Cloud more than two years ago. Since then, it’s been beta tested with hundreds of organizations—everyone from small AI startups to public multinational companies. These companies used to have long development processes for data apps that took months or quarters and multiple teams to ship. Streamlit Cloud cuts through all of that. It empowers data teams to create powerful apps in just a few hours. We’ve seen companies go from having a handful of internal apps to deploying new apps daily and creating hundreds or even thousands of apps in a year. It has completely transformed how they work with data,” said Adrien Treuille, co-founder and CEO of Streamlit.
Khronos Releases ANARI 1.0 Provisional API for Scalable 3D Data Visualization
The Khronos® Group announces the release of the ANARI™ 1.0 (Analytic Rendering Interface) provisional open standard API for scalable 3D data visualization. ANARI enables users to build the description of a scene to generate imagery, rather than specifying the details of the rendering process, providing simplified visualization application development and cross-vendor portability to diverse rendering engines, including those using state-of-the-art ray tracing. In addition to the ANARI specification, Khronos has released a sample implementation, starter applications, developer tools, and conformance tests into open source.
“A win-win for the industry, ANARI is designed to enable scalable, portable rendering that makes state-of-the-art rendering techniques and hardware-optimized renderers widely accessible, while still enabling the interactivity necessary for exploratory visualization,” said Jefferson Amstutz, ANARI working group chair and senior software engineer at NVIDIA. “Khronos anticipated this industry need and has been working on the ANARI specification for over two years to bring the 3D visualization community a well-designed, cross-platform API. Looking beyond our initial focus on scientific visualization, we believe ANARI will provide value to many developers across diverse domains that need a simpler, high-level API to render sophisticated imagery.”
AWS Announces General Availability of Babelfish for Amazon Aurora PostgreSQL
Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced the general availability of Babelfish for Amazon Aurora PostgreSQL-Compatible Edition, a new capability that allows customers to run applications written for Microsoft SQL Server directly on Amazon Aurora with little to no code changes. Babelfish for Aurora PostgreSQL enables Amazon Aurora to understand commands from applications written for Microsoft SQL Server, making it easier for customers to migrate to Amazon Aurora. With Babelfish for Aurora PostgreSQL, customers simply migrate their data and configure their application to point to Amazon Aurora, reducing costs and simplifying operations by removing the dependency on Microsoft SQL Server. Also announced today, open-source Babelfish for PostgreSQL makes the same Microsoft SQL Server language capability in Babelfish for Aurora PostgreSQL available to any organization interested in running PostgreSQL, and the source code for Babelfish for PostgreSQL is available on GitHub under the permissive Apache 2.0 and PostgreSQL licenses for anyone who wants to extend it or use it for any purpose under the terms of the license. To get started with Babelfish for Aurora PostgreSQL, visit aws.amazon.com/rds/aurora/babelfish.
“More and more customers have told us they want a fast, inexpensive, and low-risk way to break free from old-guard database vendors and their punitive licensing terms, high costs, and lack of innovation,” said Raju Gulabani, VP of Databases and Analytics at AWS. “Now, with Babelfish for Aurora PostgreSQL, anyone can quickly, easily, and cost effectively migrate their applications to Amazon Aurora, giving customers the best of both worlds—the performance and availability of the highest-grade commercial databases at a cost more commonly associated with open source.”
Kaskada Brings New Method of Time Travel to Feature Engineering
Kaskada announced the broad availability of the first-ever feature engine with time travel. The company’s approach to this methodology is vastly different from competitors, and current customers are already benefiting from improved data models, reduced risk of leakage, and significant time savings. For those in the data science field, it’s not uncommon for it to take several months to collect data and have it be outdated or even incorrect by the time it’s ready for business application. Kaskada solves this problem by letting you compute what you need directly from event-based data for feature engineering, meaning significant time and money savings for any kind of business. This method also won’t delay business value because it reveals accurate data results from the very beginning.
“The foundation of Kaskada was built on the intention to have a positive impact on the data science community, and our feature engine with time travel will do just that,” said Kaskada CEO Davor Bonaci. “This is the tool many of us in the industry have been dreaming about.”
Qlik First to Introduce True Hybrid Cloud Analytics: Qlik Forts, Bringing the Power of Cloud Analytics to Data Wherever It Resides
Qlik® announced the debut of Qlik Forts™, a new hybrid cloud service based on the Qlik Cloud® platform, that securely extends Qlik’s cloud analytics capabilities to wherever data and compute needs to reside. Whether data is located on-premises, in a virtual private cloud or a public cloud, Qlik Forts eliminates the need to move previously siloed local data for cloud analytics, ensuring the cost savings and performance benefits of SaaS while meeting every governance, jurisdiction or policy requirement. By delivering a seamless, low latency user experience across any cloud, Qlik Forts will accelerate governed use of more relevant data, a key ingredient in achieving Active Intelligence, where technology and processes trigger immediate action from real-time, up-to-date and trusted data to accelerate business value.
“Organizations aren’t experiencing the full potential of cloud analytics since much of their data remains siloed due to regulatory and sovereignty restrictions, data egress and compute costs, and laborious orchestration that results in limited user experience. All these barriers keep organizations from creating more value from all their data and realizing the benefits of Active Intelligence,” said James Fisher, Chief Product Officer at Qlik. “With Qlik Forts, we are knocking down every one of these barriers. Unlike other analytics vendors, Qlik never requires customers to move their data to our cloud. With Qlik Forts, we reinforce that flexibility and choice with the ability to deploy Qlik’s cloud analytics right next to their local data as needed, fully aligning with their existing investments, governance requirements and multi-cloud strategies.”
cnvrg.io Launches Early Access to Managed AI Platform, cnvrg.io Metacloud, Providing Ability to Run with any Storage on any Compute Solution On Demand
cnvrg.io, the operating system for artificial intelligence (AI) and machine learning (ML) built by data scientists, announced the exclusive early release of the cnvrg.io Metacloud, a new managed service enabling AI developers full flexibility, to run AI/ML workloads on a mix of infrastructure and hardware choices, even within the same AI/ML workflow or pipeline. Available platform integrations include Intel, AWS, Azure, GCP, Dell, Redhat, VMWare, Seagate and more. Exclusive early access to the cnvrg.io Metacloud is now available upon request on our website.
“AI has yet to meet its ultimate potential by overcoming all the operational complexities. The future of machine learning is dependent on the ability to deliver models seamlessly using the best infrastructure available”, said Yochay Ettun, CEO and Co-Founder of cnvrg.io. “cnvrg.io Metacloud is built to give flexibility and choice to AI developers to enable successful development of AI instead of limiting them, so enterprises can realize the full benefits of machine learning sooner.”
Speechmatics achieves AI breakthrough, beating tech giants in tackling bias and inclusion to understand all voices
Speechmatics, a leading speech recognition technology scaleup, has launched its ‘Autonomous Speech Recognition’ software. Using the latest techniques in deep learning and with the introduction of its breakthrough self-supervised models, Speechmatics outperforms Amazon, Apple, Google and Microsoft in the company’s latest step towards its mission to understand all voices. Based on datasets used in Stanford’s ‘Racial Disparities in Speech Recognition’ study, Speechmatics recorded an overall accuracy of 82.8% for African American voices compared to Google (68.7%) and Amazon (68.6%). This level of accuracy equates to a 45% reduction in speech recognition errors – the equivalent of three words in an average sentence. Speechmatics’ Autonomous Speech Recognition delivers similar improvements in accuracy across accents, dialects, age, and other sociodemographic characteristics.
“We are on a mission to deliver the next generation of machine learning capabilities, and through that offer more inclusive and accessible speech technology,” commented Katy Wigdahl, CEO of Speechmatics. “This announcement today is a huge step towards achieving that mission. Our focus in tackling AI bias has led to this monumental leap forward in the speech recognition industry and the ripple effect will lead to changes in a multitude of different scenarios. Think of the incorrect captions we see on social media, court hearings where words are mis-transcribed and eLearning platforms that have struggled with children’s voices throughout the pandemic. Errors people have had to accept until now can have a tangible impact on their daily lives.”
Franz’s AllegroGraph 7.2 Powers Enterprise Data Fabrics with Graph Neural Networks, Virtual Graphs and Streaming Graph Pipelines
Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, announced AllegroGraph 7.2, which provides organizations with essential Data Fabric tools, including Graph Neural Networks, Graph Virtualization, Apache Spark graph analytics, and streaming graph pipelines. These new capabilities exemplify AllegroGraph’s leadership in empowering data analytics professionals to derive business value out of Knowledge Graphs.
“The ability to create Graph Neural Networks within the AllegroGraph platform opens up the next level of AI to data analytics professionals with the ability to produce the best prescriptive outcomes,” said Dr. Jans Aasman, CEO of Franz Inc. “GNNs are ideal for applying machine learning’s advanced pattern recognition to high-dimensional, non-Euclidian datasets that are too complex for other machine learning types. Organizations get two forms of reasoning in one framework by fusing GNN reasoning capabilities around relationship predictions, entity classifications, and graph clustering, with classic semantic inferencing available in AllegroGraph Knowledge Graphs. Automatically mixing and matching these two types of reasoning is next level AI and is the basis for predicting the best prescriptive outcome for any business event based on context at scale.”
BrainChip Begins Taking Orders of Akida AI Processor Development Kits
BrainChip Holdings Ltd (ASX: BRN), (OTCQX: BRCHF), a leading provider of ultra-low power high performance artificial intelligence technology, announced BrainChip will be taking orders of two development kits for its Akida™ advanced neural networking processor, enabling partners, large enterprises, and OEMs to begin internal testing and validation of Akida’s high-performance, small, ultra-low power AI chip. Akida NSoC and intellectual property enable a wide array of edge AI capabilities that include continuous learning and inference. BrainChip is offering two development kits both including the AKD1000 chip on a mini-PCI board: an X86 Shuttle PC development kit as well as an ARM-based Raspberry Pi development kit.
“Offering development kits is not only a major step towards full commercialization, it’s also an exciting opportunity to see how our partners and future customers will put Akida to work in environments and scenarios like consumer electronics, industrial applications, aerospace and defense systems, healthcare and medical devices, automotive technology, and more,” said Anil Mankar, BrainChip co-founder and chief development officer. “We believe the AKD1000 silicon, or the licensing of Akida in a configurable IP format, will lead to major changes in industries using AI at the edge because of its performance, security, low power requirements, and mainly Akida’s ability to perform AI training and learning on the device itself, without dependency on the cloud.”
Datatron Introduces New Features to MLOps and AI Governance Solution
Datatron announced enhancements to its MLOps and AI governance solution, making it even easier for enterprises to catalog, operationalize, monitor and govern AI/ML models. With Datatron, customers experience 15 to 20 times more effectiveness in model deployment, bringing substantial business gains and productivity improvements. Datatron also eliminates the complexity and expense associated with constant iteration and management of many AI models at one time.
“Despite all the readily available open source MLOps frameworks, building your own MLOps infrastructure from scratch is no trivial task,” said Harish Doddi, CEO, Datatron. “Constant iteration and management of many AI models can be incredibly complex and expensive. That’s why we’re dedicated to making it even easier than ever for enterprises to operationalize, monitor and govern a large number of AI models.”
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