TELUS International announced the completion of its previously announced acquisition of a 100% interest in Lionbridge AI, following the clearance of the acquisition by the Committee on Foreign Investment in the United States (CFIUS). Lionbridge AI is a global provider of scalable data annotation services for text, images, videos, and audio. The company sources multilingual training data in more than 300 languages and dialects to build premium, ground truth data for some of the world’s largest technology companies in social media, search, retail and mobile. Lionbridge AI has also developed a proprietary data annotation platform that is used in combination with a crowdsourced community of one million professional annotators, qualified linguists and in-country language speakers across six continents.
Cloudera, an enterprise data cloud company, announced the availability of the Cloudera Data Platform (CDP) Operational Database on both Amazon Web Services (AWS) and Microsoft Azure. CDP Operational Database is a fully managed cloud-native operational database with scale, performance, and reliability. Optimized to be deployed anywhere, on any cloud platform, CDP Operational Database aligns with the cloud infrastructure strategy best suited for the business.
CDP Operational Database works across public and private cloud environments, including on-premises. It enables application developers to deliver prototypes in under an hour on their choice of cloud, with the power to easily scale to petabytes of data. Application developers can deliver mission-critical applications with speed because CDP Operational Database auto-scales, auto-heals and auto-tunes based on workload needs. CDP Operational Database is now generally available on AWS and Azure.
DataStax announced the general availability of Astra serverless, an open, multi-cloud serverless database-as-a-service (DBaaS). DataStax’s Astra will deliver pay-as-you-go data together with multi-cloud and open source. DataStax Astra builds upon the Apache Cassandra open source database and introduces a modern, microservices-based architecture that separates compute from storage, enabling database resources to scale up and down on demand to match application requirements and traffic independent of compute resources.
While serverless compute has been around for a while, serverless data has lagged due to the technical challenges around separating compute and storage. Scaling a database typically requires the addition of more server nodes in order to handle more demand or to store more data, which, in turn, requires that the entire data set is “rebalanced” across the nodes to keep the ratio of storage and computing capability equal. With the introduction of Astra serverless, developers will only pay for what they use, no matter how many database clusters they create and deploy. This flexibility brings faster application development and streamlined operations by letting developers and IT create as many databases as they need for development, testing, staging, or any other purpose.
MongoDB, Inc. and Google Cloud announced an expanded five-year partnership that will extend their existing go-to-market relationship and provide a deeper integration of Google Cloud products with MongoDB’s global cloud database, MongoDB Atlas. As a fully-managed service directly integrated with the Google Cloud Console and Marketplace, MongoDB Atlas gives joint customers integrated billing and support. Customers get a single bill for all Google Cloud services as well as MongoDB Atlas, and can use their Google Cloud spending commitments toward Atlas. The service is now available as a “pay as you go” offering on the Google Cloud Marketplace.
With this expanded partnership, MongoDB is enabling developers to integrate Atlas with Google Cloud products, including Pub/Sub, BigQuery, Dataproc, Dataflow, Cloud Run, App Engine, Cloud Functions, Google Kubernetes Engine (GKE), and Tensorflow. Additionally, Google Cloud’s mainframe modernization solutions now support MongoDB Atlas and help customers convert legacy COBOL code on mainframes into modern Java-based applications built on MongoDB. Together, G4 and MongoDB Atlas accelerate the modernization and migration process for organizations moving their business-critical workloads to the cloud.
dotData, provider of data science automation and operationalization for the enterprise, announced dotData Cloud, AI-automation software platform and services designed to provide business intelligence (BI) teams with the ability to automate AI/ML development. The solution was developed specifically to support growing enterprise organizations with small or no data science teams who want to launch or accelerate their data science practice. dotData Cloud is currently available as an all-inclusive bundle that includes technology and support to assist organizations in developing and deploying their first model, with a 45-day, risk-free, no-money guarantee.
dotData’s AI-powered feature engineering automatically discovers, evaluates and features by transforming hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects that are fundamental to developing predictive analytics solutions. It also democratizes data science by enabling BI developers and data engineers to make enterprise data science scalable and sustainable. dotData is also designed to operationalize AI/ML models by producing both feature and ML scoring pipelines in production, which IT teams can then quickly integrate with business workflows. With the dotData GUI, AI/ML development becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.
Appfire, a provider of apps for software development teams, announced the acquisition of Bolo Software, an Atlassian Marketplace application provider. As part of the acquisition, Bolo Software founder, Jason Boileau, will join Appfire’s team of technologists. Jason brings several years of experience in developing products that help teams create and share content in Confluence. Since 2012, Bolo Software has created solutions that help organizations use Atlassian products to their full potential. Bolo’s line of publication apps includes LaTeX Math, a popular solution that provides enterprise teams with math formatting on Confluence pages including adding equations and units to pages. Other Bolo Software products include Numbered Captions and Easy Numbered Headings.
Fulcrum announced a new AI-based, privacy-protecting capability within their no-code mobile application platform that automatically detects objects in photos that mobile workers collect in the field. In addition to leveraging information about physical assets to optimize workflows and performance reporting, customers can set Fulcrum to obscure any faces it detects using graphical blurring. Fulcrum enables mobile workers to take photos that document safety, quality, environmental, and other processes. By using AI to automatically detect objects in those photos, Fulcrum enables new flows of information based on the objects that are in the work environment.
In some cases, photographic data collected using Fulcrum meets the definition of personally identifiable information (PII) according to privacy regulations such as GDPR and CCPA. Photographic data can be among the most challenging to manage from a data privacy perspective. Photos aren’t intended to capture PII, but a face in a photo can be used to identify a specific person and therefore is considered PII as defined by most global privacy regulations. Fulcrum enables organizations to screen photos for faces automatically and blur them without manual effort. This screening takes place on the mobile device where the inspection or other data collection is taking place, ensuring that no unblurred faces are transmitted into the cloud or stored in a server.
Melax Technologies announced the release of LANN, a text annotation, natural language processing (NLP) product for AI-assisted, team-based projects. Based on NLP and artificial intelligence (AI) technology, Melax Tech’s text annotation capabilities combine automated machine learning workflows with client input to create high-quality annotated datasets. LANN is deployed in multiple use cases in the medical field, including phenotyping algorithms, clinical chart review, and drug repurposing. LANN was developed to assist users to comb through professional journals and literature to develop a myriad of projects, within the life sciences and across commercial and academic disciplines.
LANN’s functionality supports client projects by providing a rich array of team-based communication and sophisticated quality control tools. This allows teams to review results and make adjustments that result in superior annotation outcome. The resulting datasets provide high performance including use in downstream artificial intelligence or machine-learning applications. Standard and enterprise versions allow commercial and large-scale entities to build large-team projects, programs and custom solutions. With flexible pricing options, Melax Tech provides clients with the advantage to grow research projects over time affordably. Melax Technologies also announced that LANN is available at no cost for educational projects.