Sentient Technologies Raises $103.5M In Series C Funding For Distributed Artificial Intelligence Technology

Artificial intelligence vendor Sentient Technologies recently announced the finalization of $103.5M in Series C funding in a round led by Tata Communications (Hong Kong), existing investor Horizons Ventures and a group of private investors. Sentient’s technology features an artificial intelligence and machine learning platform that operates on distributed datasets to develop actionable business intelligence from disparate, asynchronous data sources. The company’s patent pending technology has thus far been used to develop analytic insights in the financial and healthcare industries. Sentient differentiates itself in the artificial intelligence space by way of its unique ability to scale to process artificial intelligence jobs on millions of nodes in parallel.

Vinod Kumar, MD and CEO of Tata Communications, the company that led the Series C round, remarked on the significance of Sentient Technologies as follows:

As an investor, we share a common vision on the transformative force that massively distributed computing and artificial intelligence can play in helping businesses get insights and solve their most complex big data problems. We see Sentient at the forefront of these technologies and bringing a disruptive approach to cloud based computing services. Furthermore, the scale of our leading global network infrastructure and data center footprint also complements Sentient’s growth plans and will enable its global deployment.

Here, Kumar positions Sentient Technologies as contributing to the “transformative force that massively distributed computing and artificial intelligence” currently plays in revolutionizing the way in which businesses manage big data analytics. Sentient delivers a “disruptive” approach to cloud-based distributed artificial intelligence that benefits from its collaboration with Tata’s global data center and network infrastructure. As such, Sentient participates in a resurgence of artificial intelligence technologies as evinced by IBM’s $100M venture fund in Watson supercomputing, Google’s acquisition of DeepMind technologies for $500M and early stage artificial intelligence startups such as Wit.ai, Idibon, Expect Labs and Prediction IO. Given that Sentient’s Series C funding represents the largest venture round funding investment in an artificial intelligence startup to date, the industry should expect more details of its technology platform and product roadmap to emerge in upcoming months. Sentient’s platform differentiates by way of its distributed artificial intelligence technology and massive ability to scale, although details of its predictive analytics and data management technology have yet to emerge. For now, however, the bottom line is that AI is hot both for investors and prospective customers that are increasingly interested in leveraging iterative machine learning technologies into business operations.

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BigPanda Emerges From Stealth To Manage Deluge Of IT Alerts And Notifications

BigPanda today launches from stealth to tackle the problem of managing the explosion of alerts and notifications that IT administrators increasingly receive, daily, from myriads of applications and devices. The Mountain View-based startup integrates alerts and notifications from disparate sources into a consolidated data feed that parses unstructured data into structured data to create an aggregated alerts and notifications data repository. BigPanda’s proprietary analytics subsequently run against the integrated data repository to enable the creation of topologies and relationships, time-based analytics and statistical analytics as indicated by the screenshot of an incident dashboard below:

Examples of statistical analytics include probabilistic determinations that the concurrent appearance of notification A, B and C is likely to lead to outcome X as suggested by historical data about the conjunction of the notifications in question. The platform’s machine-learning technology incrementally refines its analytics in relation to incoming data and thereby iteratively delivers more nuanced analyses and visualizations of notifications-related data. Overall, the platform enables customers to more effectively manage the tidal wave of data from notifications that bombard the inboxes of IT administrators by facilitating the derivation of actionable business intelligence based on the aggregation of notifications from discrete systems and applications.

As told to Cloud Computing Today by BigPanda CEO Assaf Resnick, the platform integrates with monitoring systems such as New Relic, Nagios and Splunk and additionally provides REST API functionality to connect to different applications, deployment infrastructures and ITSM tools. Moreover, BigPanda today announces the finalization of $7M in Series A funding in a round led by Mayfield with additional participation from Sequoia Capital. The $7M funding raise brings the total capital raised by BigPanda to $8.5M, following upon a $1.5M pre-Series A seed round of funding from Sequoia Capital. Deployed as a SaaS application that runs on AWS infrastructure while leveraging a MongoDB NoSQL datastore, BigPanda fills a critical niche in the IT management space by delivering one of the few applications aimed at consolidated notification management and analytics. As applications, infrastructure components and networking devices proliferate with dizzying complexity in the contemporary datacenter, platforms like BigPanda are likely to morph into necessary components of IT management as a means of taming the deluge of notifications produced by disparate systems. Meanwhile, BigPanda’s early positioning in the notification-management space renders it a thought leader as well as a technology standout.

MapR Finalizes $110M In Equity And Debt Financing Led By Google Capital And Silicon Valley Bank

On Monday, MapR Technologies announced the finalization of $110M in funding based on $80M in equity financing and $30M in debt financing. Google Capital led the equity funding in collaboration with Qualcomm Incorporated, Lightspeed Venture Partners, Mayfield Fund, NEA and Redpoint Ventures while MapR’s debt funding was financed by Silicon Valley Bank. The funding will be used to spearhead MapR’s explosive growth in the Hadoop distribution and analytics space as illustrated by a threefold increase in bookings in Q1 of 2014 as compared to 2013. Gene Frantz, General Partner at Google Capital, commented on Google Capital’s participation in the June 30 funding raise as follows:

MapR helps companies around the world deploy Hadoop rapidly and reliably, generating significant business results. We led this round of funding because we believe MapR has a great solution for enterprise customers, and they’ve built a strong and growing business.

Monday’s announcement comes soon after MapR’s news of its support for Apache Hadoop 2.x and YARN in addition to all five components of Apache Spark, the open source technology used for big data applications that specialize in interactive analytics, real-time analytics, machine learning and stream processing. The additional $110M in funding strongly positions MapR with respect to competitors Cloudera and Hortonworks given that Cloudera recently raised $900M and Hortonworks finalized $100M in funding. The news of MapR’s $110M funding also coincides with a recent statement from Hortonworks certifying the compatibility of YARN with Apache Spark as part of a larger announcement about the integration of Spark into the Hortonworks Data Platform (HDP) alongside its Hadoop security acquisition XA Secure and Apache Ambari for the provisioning and management of Hadoop clusters. With a fresh round of capital in the bank and backing from Google, the creators of MapReduce, MapR signals that the battle for Hadoop market share features a three horse race that is almost certain to intensify as vendors compete to streamline and simplify the operationalization of Big Data. In the meantime, Big Data-related venture capital continues to flow like water bursting out of a fire hydrant as the Big Data space tackles problems related to big data analytics, streaming big data and Hadoop security.

Neo4j Adopted By Retail Giants eBay and Walmart For Real-Time, E-commerce Analytics

Neo Technology recently announced that retail giants such as eBay and Walmart are using graph database Neo4j in production-grade applications that improve their operations and marketing analytics. In a recently published case study, Neo Technology revealed how eBay’s e-commerce technology platform acquisition, Shutl, leverages Neo4j to expedite delivery to the point where customers can enjoy same day delivery in select cases. Shutl constitutes the technology platform that undergirds eBay Now, a service that delivers products in 1-2 hours from local stores by means of relationships between couriers and stores. eBay decided to make the transition from MySQL to Neo4j because:

Its previous MySQL solution was too slow and complex to maintain, and the queries used to calculate the best route additionally took too long. The eBay development team knew that a graph database could be added to the existing SOA and services structure to solve the performance and scalability challenges. The team turned to Neo4j as the best possible solution on the market.

According to Volker Pacher, Senior Developer at eBay, eBay found that Neo4j enabled dramatic improvements in its computational and querying ability:

We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.

eBay’s current ecommerce technology platform leverages Ruby, Sinatra, MongoDB, and Neo4j. Importantly, queries “remain localized to their respective portions on the graph” in order to ensure scalability and performance. Walmart, meanwhile, uses Neo4j to understand the online habits of its shoppers in order to deliver more relevant real-time product recommendations for their online shoppers. Neo4j’s adoption by eBay and Walmart symptomatically illustrates how graph databases are disrupting the nature of real-time analytics, a trend further underscored by Pivotal HD 2.0’s integration of GraphLab into its offerings, and the use of graphing technologies by startups such as Aorato.