Category Archives: Machine learning

Enterprise IT, HPE, Machine learning, Microsoft Azure, News & Analysis

HPE launches ‘machine-learning-as-a-service’ on Microsoft Azure

HPE office logoHPE has upgraded its Haven OnDemand proposition to deliver it as ‘machine learning as a service’ via Microsoft Azure.

The product offers a freemium model and has collected around 12,000 registered developers since the beta launch in 2014. Through the leadership of Haven OnDemand CTO, Chris Goodfellow, the service is built on the mantra of ‘the sum is greater than the parts’, utilizing more than 60 API’s which combine to provide machine learning capabilities.

“The software industry is on the cusp of a new era of breakthroughs, driven by machine learning that will power data-driven applications across all facets of life,” said Colin Mahony, GM of HPE Big Data. “HPE Haven OnDemand democratizes big data by bringing the power of machine learning, traditionally reserved for high-end, highly trained data scientists, to the mainstream developer community”

Haven OnDemand includes features designed for applications such as sentiment analysis in text, text extraction from images, face and logo recognition, social media analysis and speech recognition. Developers can also build a set of self-learning functions that analyze, predict and alert based on structured datasets. French start-up Ayni utilized the speech recognition API to help it create text transcripts of live audio streams on its foreign language education app.

Alongside the product development over the last 12 months, HPE has also run an active global hackathon program, which has provided feedback to help optimize the offering.

All HPE Haven OnDemand APIs and services are hosted on Microsoft Azure, building on the long-term strategic partnership between the two tech giants. Back in December, the partnership was extended as HPE appointed Microsoft Azure as a preferred public cloud partner. In return, HPE was granted preferred partner status in providing infrastructure and services for Microsoft hybrid cloud offerings.

“Organizations have massive quantities of information that can hold insights into business transformation, but harnessing it can be challenging,” said Garth Fort, General Manager, Partner and Channel Marketing, Cloud and Enterprise at Microsoft. “Leveraging the high performance and scalability of Azure, HPE Haven OnDemand brings our mutual customers a compelling solution to help turn their data into value.”

M&A, M2M, Machine learning, News & Analysis, PredictionIO, Salesforce

Salesforce bolsters machine learning business with PredictionIO acquisition

AI-Artificial-Intelligence-Machine-Learning-Cognitive-ComputingOpen source machine learning software vendor PredictionIO has announced it is to become part of Salesforce. The Palo Alto start up has stressed that the software will continue to be available under an open source Apache license.

The addition of analytics and machine learning has become a key strategy to Salesforce as it bids to build on its cloud offerings. Last year BCN reported how Salesforce was adding new Wave Actions to its Analytics Cloud intelligence tool. More recently it bought machine learning companies RelateIQ and Tempo AI and integrated staff into its data science teams.

Machine learning, which can be used in many cloud applications, has become an area of contention in the cloud industry with other start ups in this area, such as H2O and Skytree, the subject of takeover rumours.

California based PredictionIO was formed in 2013 and a year later received $2.5 million in backing from investors including Azure Capital Partners. Other backers include CrunchFund, the Stanford-StartX Fund and Kima Ventures. Dropbox is PredictionIO’s most prominent client.

CEO Simon Chan explained the rationale for selling the firm on his company blog. As part of Salesforce, PredictionIO’s machine learning system will get immediate access to the entire Salesforce clouds. The opportunity to extend SalesforceIQ’s machine learning and intelligence was a chance not to be passed up, he said. “Being a part of Salesforce will give us an amazing opportunity to continue building our open source machine learning platform on a much larger scale,” said Chan.

Chan’s objective will be the same within Salesforce – to simplify development of machine learning technology and build it up. PredictionIO now has 8,000 developers creating over 400 apps. Chan pledged that PredictionIO’s open source technology will stay that way and will continue to be free to all users. To mark the Salesforce deal it is to dropping the PredictionIO Cluster software fee on AWS Cloudformation, which will is now free for the first time in the company’s history.

IBM, Machine learning, Networks, News & Analysis, Open Source, SystemML

IBM open-sources machine learning SystemML

Machine 2 MachineIBM is aiming to popularise its proprietary machine learning programme SystemML through open-source communities.

Announcing the decision to share the system source code on the company blog, IBM’s Analytics VP Rob Thomas said application developers are in need of a good translator. This was a reference to the huge challenges developers face when combining information from different sources into data-heavy applications on a variety of computers, said Thomas. It is also a reference to the transformation of a little used proprietary IBM system into a popular, widely adopted artificial intelligence tool for the big data market. The vehicle for this transformation, according to Thomas, will be the open-source community.

IBM claims SystemML is now freely available to share and modify through the Apache Software Foundation open-source organisation. Apache, which manages 150 open-source projects, represents the first step to widespread adoption, Thomas said. The new Apache Incubator project will be code named Apache SystemML.

The machine learning platform originally came out of IBM’s Almaden research lab ten years ago when IBM was looking for ways to simplify the creation of customized machine-learning software, Mr. Thomas said. Now that it is in the public domain, it could be used by a developer of cloud based services to create risk-modeling and fraud prevention software for the financial services industry, Thomas said.

The current version of SystemML could work well with Apache project Spark, Thomas said, since this is designed for processing large amounts of data that stream in from continuous sources like monitors and smartphones. SystemML will save companies valuable time by allowing developers to write a single machine learning algorithm and automatically scale it up using open-source data analytics tools Spark and Hadoop.

MLLib, the machine learning library for Spark, provides developers with a rich set of machine learning algorithms, according to Thomas, and SystemML enables developers to translate those algorithms so they can easily digest different kinds of data and to run on different kinds of computers.

“We believe that Apache Spark is the most important new open-source project in a decade. We’re embedding Spark into our Analytics and Commerce platforms, offering Spark as a service on IBM Cloud, and putting more than 3,500 IBM researchers and developers to work on Spark-related projects,” said Thomas.

While other tech companies have open-sourced machine learning technologies they are generally niche specialised tools to train neural networks. IBM aims to popularise machine learning within Spark or Hadoop and its ubiquity will be critical in the long run, said Thomas.

Analytics, cloud security, Funding, Machine learning, News & Analysis, Norwest Venture Partners (NVP), Palerra, Wing Venture Capital and Engineering Capital

Cloud security vendor Palerra scores $17m

Palerra is among a number of cloud security startup combining predictive analytics and machine learning algorithms to bolster cloud security

Palerra is among a number of cloud security startups combining predictive analytics and machine learning algorithms in clever ways

Cloud security vendor Palerra has secured $17m in series B funding, a move the company said would help accelerate sales and marketing efforts around its predictive analytics and threat detection services.

Palerra’s flagship service, Loric, combines threat detection and predictive analytics in order to provide automatic incident response and remediation for malicious traffic flowing to a range of cloud services and platforms.

Over the past few years we’ve seen a flurry of cloud security startups emerge, which all deploy analytics and machine learning algorithms to cleverly detect perceived and actual threats and respond in real-time, so it would seem enterprises are starting to become spoilt with choice.

The $17m round was led by August Capital, with participation from current investors Norwest Venture Partners (NVP), Wing Venture Capital and Engineering Capital, and brings the total amount secured by the firm to $25m.

The funds will be used to bolster sales and marketing efforts at the firm.

“The dramatic rise in adoption of cloud services by today’s enterprises against the backdrop of our generation’s most potent cyber threats has necessitated a new approach. LORIC was designed to meet these threats head on and this new round underscores our commitment to deliver the most powerful cloud security solution in the industry,” said Rohit Gupta, founder and chief executive officer of Palerra.

“As the perimeter disintegrates into a set of federated cloud-based and on-premises infrastructures, effective monitoring becomes almost impossible, unless security controls are embedded in these heterogeneous environments. This will require enterprises to reconsider and possibly redesign their security architecture and corresponding security controls by placing those controls in the cloud,” Gupta added.

Analytics, Artificial intelligence, Cloud & IT Management, Machine learning, Partnerships, Prelert, Splunk

Anomaly Detective Adds Predictive Analytics to Splunk

Prelert today announced Anomaly Detective, an advanced machine intelligence solution for Splunk Enterprise environments. The introduction of Anomaly Detective expands Prelert’s line of diagnostic predictive analytics products that integrate with a customer’s existing IT management tools and quickly provide value by finding problematic behavior changes hidden in huge volumes of operations data.

Anomaly Detective’s self-learning predictive analytics with machine intelligence assistance recognize both normal and abnormal machine behavior. Using highly advanced pattern recognition algorithms, Anomaly Detective identifies developing issues and provides detailed diagnostic data, enabling IT experts to avoid problems or diagnose them as much as 90 percent faster than previously possible. IT personnel who utilize Splunk Enterprise software in infrastructure, applications performance and security can now additionally benefit from machine learning to automatically spot anomalies and isolate their root causes in minutes, saving time and resolving problems before the business is impacted.

Anomaly Detective is  downloadable software that installs as a tightly integrated application for Splunk Enterprise. Because it leverages recent advances in machine intelligence, Anomaly Detective is 100 percent self-learning and requires minimal configuration. Anomaly Detective augments existing IT expertise, empowering IT staff to spend less time mining data, reduce troubleshooting costs and improve compliance with service-level agreements — all of which contribute to a rapid return on investment.

“Prelert Anomaly Detective is like a machine intelligence assistant, using advanced machine learning analytics to analyze the massive amounts of IT operations management data produced by today’s online applications and services,” said Mark Jaffe, CEO of Prelert. “We’ve packaged the power of big data analytics, normally focused on solving business problems, in easy-to-use machine intelligence solutions that are greatly needed in the real world of IT operations.”

Prelert Anomaly Detective is now available and easily downloadable from the Prelert website and from Prelert resellers. Pricing is based on the amount of data analyzed per day, starting at $1,200 for environments indexing more than 500MB of data per day. For information on pricing for Splunk Enterprise, go to http://www.splunk.com/view/how-to-get-splunk/SP-CAAADFV.