Amazon Machine Learning Renders Amazon’s Analytic Tools More Broadly Available

Amazon Web Services recently announced the availability of machine learning technology that allows developers to create predictive analytics by using the same algorithms that Amazon uses to manage its supply chain inventory and operations. Amazon’s Machine Learning platform empowers developers and data scientists with the ability to identify patterns and predictive analytics for data stored in Amazon S3, Amazon RedShift and Amazon Relational Database Service. By using Amazon Machine Learning, developers can obtain analytic insights without writing custom-built predictive analytics-based applications that require complicated scripting, debugging, code deployment and application management. In addition to enjoying the benefits of preconfigured machine learning libraries and wizards that accelerate access to pattern recognition and predictive analytics, Amazon Machine Learning enables customers to enjoy the benefits of a scalable platform capable of generating billions of predictions per day in real-time. Popular use cases for Amazon’s Machine Learning technology include the detection of fraud, the ability to personalize web-related content and deliver targeted marketing campaigns that iteratively become more effective in conjunction with the evolving sophistication of the predictive model. Amazon’s Machine Learning platform competes directly with Microsoft Azure’s Machine Learning platform that was released in public preview in July 2014. By rendering available the same technologies used by Amazon’s data scientists, Amazon adds yet another incentive for customers to leverage its ever expanding portfolio of cloud and big data products and services. The increasing availability of machine learning technologies underscores the democratization of analytics enabled by the contemporary cloud and big data revolution, even though many of the solutions available on the market remain proprietary and attached to usage of a larger IaaS platform.

Alation Comes Out Of Stealth With Platform Designed To Enhance Access To Data Within An Organization

Alation today came out of stealth to announce a software platform that enables knowledge workers within enterprises to more effectively locate, understand and use data stored within their data infrastructures. Alation integrates knowledge of an organization’s data by using a combination of machine learning technology in conjunction with human curation processes to help organizations derive the maximum value from their data repository. The company’s technology platform crawls an organization’s databases to create the analogue of a Google PageRank for data elements that reflect how often the data has been used, who has used it, who originated the data and the degree to which it has been validated within the organization. Alation’s platform also features collaborative analytics that allow analysts to take advantage of the crowdsourced contributions of other analysts and data scientists. The collaborative analytics functionality enables users to take advantage of real-time annotations to data elements from other users within the organization that shed light on its significance, uses, reception and relationship to other data points and sets.

Other solutions offered by Alation include Data Warehouse Optimization, Data Governance frameworks and Data Search and Discovery that runs across distributed datasets by using natural language searches. Today’s announcement builds on Alation’s $9M Series A funding raise announcement in March. Alation already claims an impressive roster of customers that include eBay, MarketShare, Inflection and Invoice2Go. Unlike the bevy of Big Data analytics platforms that focus on analytic queries on streaming or distributed datasets, Alation’s innovation consists in its commitment to simplifying, streamlining and enriching access to data within an enterprise. By focusing on knowledge about data, Alation promises to accelerate both the quality of analytic deliverables as well as the cycle time required to produce data-driven insights by rendering data easier to find and contextualize. Expect to hear more about Alation’s ability to leverage machine learning technology to enhance the ability of analysts to locate data of interest as its platform consolidates on its early market traction and focuses the valences of its product development in collaboration with customer feedback.

Glassbeam Integrates With Apache Spark And Enhances Its Analytics And Machine Learning Functionality

Santa Clara-based machine data analytics vendor Glassbeam recently revealed details of a new version of Glassbeam SCALAR marked by deep integration with Apache Spark. Apache Spark is a parallel data processing framework that facilitates real-time analytics, machine learning and real-time analytics by storing the results of data operators in memory and performing low latency, iterative calculations on in memory computational results. Known for its ability to automate the parallelization of tasks and jobs, Spark boasts operational efficiencies over MapReduce by a factor of 100 with respect to the execution of calculations on large datasets. Glassbeam SCALAR’s integration with Apache Spark enhances its computational capabilities as well as the platform’s machine learning functionality and capacity to perform real-time analytics on streaming datasets by means of the Spark Streaming and MLLib components of the Spark stack. Built on Cassandra, Spark’s addition to the Glassbeam’s cloud analytics platform gives it the benefits of Cassandra’s distributed data management architecture in addition to Spark’s computational, analytic and machine learning functionality. As such, today’s announcement strengthens Glassbeam’s position in the nascent but exploding internet of things analytics space by augmenting its ability to ingest, process and analyze massive amounts of data as well as enhancing Glassbeam SCALAR’s advanced analytics, machine learning and predictive analytics capabilities.

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.

Microsoft Azure Reveals Azure ML, Cloud-Based Platform For Machine Learning And Predictive Analytics

Microsoft recently announced details of Azure ML, a platform for machine learning hosted on the Microsoft Azure cloud. Azure ML enables organizations to rapidly predict future trends such as crime, disease outbreaks, weather and traffic patterns. Whereas machine-learning and predictive analytics currently tend to be managed through on premise installations, Azure ML accelerates the pace with which data teams can obtain insights derived from historical data by making available a fully managed, scalable platform for machine learning that allows customers to focus on developing and refining predictive analytic parameters and algorithms without the burden of provisioning, managing and optimizing the infrastructure on which the applications are hosted. Azure ML will come pre-configured with “visual workflows and startup templates” that accelerate the process of developing predictive analytics. Moreover, the Azure ML platform will allow customers to expeditiously publish web services and APIs to facilitate collaboration between geographically dispersed teams. Currently, MAX451 is using a preview version of Azure ML to determine what retail customers are likely to purchase next while Carnegie Mellon is using the platform to understand variations in energy output across buildings on its university campus. Azure ML will be released in a public preview mode in July.

StackStorm Emerges From Stealth To Deliver Artificial Intelligence To DevOps Technology For Cloud Infrastructures

Today, StackStorm emerged from stealth mode and revealed details of a DevOps solution for IaaS cloud environments, with a specific focus on OpenStack at the present time. In much the same vein that Pivotal sought to bring the computing power, scalability and operational efficiencies of enterprises such as Facebook, Google, Yahoo and Twitter to mainstream enterprise IT, StackStorm proposes to bring automation technology analogous to that used by companies like Facebook to enterprises, SMBs and startups alike. StackStorm CEO Evan Powell elaborated on the company’s technology by noting that, “the world’s top cloud infrastructure operators are 10-100x more productive than the average operator thanks in part to homemade operations automation like Facebook’s FBAR. We built StackStorm to deliver exactly this kind of software and productivity boost to the broader market.” In a phone interview with Cloud Computing Today, Powell noted further that the platform specialized in simplifying the automation of workflows in addition to leaving an audit trail regarding the implementation of automation. Moreover, the StackStorm platform integrates machine learning into its product in order to render its automation technology more intelligently and intuitively responsive to the evolving needs of the infrastructure in question. Cofounded by by Evan Powell and Dmitri Zimine, StackStorm’s mission involves delivering automation and artificial intelligence to the operation of datacenters and cloud-based infrastructures, with a particular emphasis on empowering companies who lack top-tier DevOps talent to automate their workflows with efficacy and transparency.