13 frameworks for mastering machine learning

H2O, now in its third major revision, provides access to machine learning algorithms by way of common development environments (Python, Java, Scala, R), big data systems (Hadoop, Spark), and data sources (HDFS, S3, SQL, NoSQL). H2O is meant to be used as an end-to-end solution for gathering data, building models, and serving predictions. For instance, models can be exported as Java code, allowing predictions to be served on many platforms and in many environments.

H2O can work as a native Python library, or by way of a Jupyter Notebook, or by way of the R language in R Studio. The platform also includes an open source, web-based environment called Flow, exclusive to H2O, which allows interacting with the dataset during the training process, not just before or after. 

Source: InfoWorld Big Data

EvoSwitch Releases White Paper

EvoSwitch has released a new white paper titled: ‘How to Build a Better Cloud –Planning.’ Aimed at CIOs, CTOs and IT Directors, the white paper provides expert-input to a business-driven planning process for weighing multi-cloud environments and implementing a hybrid cloud strategy.

As a colocation services provider with its data centers located in Amsterdam, the Netherlands, and Manassas (Washington DC area) in the U.S., EvoSwitch serves a considerable amount of clients with hybrid cloud needs. That’s why the colocation company established its cloud marketplace, EvoSwitch OpenCloud, two years ago. Through this marketplace, EvoSwitch customers would be able to quickly and securely interconnect to a large number of other cloud platforms including AWS, Google and Azure.

Partly based on these OpenCloud, hybrid cloud customer experiences as well as cloud management expertise of the author himself, the EvoSwitch white paper released today provides CIOs, CTOs, and IT Directors with business-driven guidance for successfully planning their hybrid cloud strategy. Titled ‘How to Build a Better Cloud –Planning,’ the white paper is written by seasoned data center services and cloud computing professional, Patrick van der Wilt, who serves as the commercial director for EvoSwitch.

EvoSwitch’s new white paper ‘How to Build a Better Cloud –Planning’ counts 30 pages and is available in English. It can be downloaded for free here.

Source: CloudStrategyMag

How to use Apache Kafka messaging in .Net

Apache Kafka is an open source, distributed, scalable, high-performance, publish-subscribe message broker. It is a great choice for building systems capable of processing high volumes of data. In this article we’ll look at how we can create a producer and consumer application for Kafka in C#.

To get started using Kafka, you should download Kafka and ZooKeeper and install them on your system. This DZone article contains step-by-step instructions for setting up Kafka and ZooKeeper on Windows. When you have completed the setup, start ZooKeeper and Kafka and meet me back here.

Apache Kafka architecture

In this section, we will examine the architectural components and related terminology in Kafka. Basically, Kafka consists of the following components:

• Kafka Cluster—a collection of one or more servers known as brokers
• Producer – the component that is used to publish messages
• Consumer – the component that is used to retrieve or consume messages
• ZooKeeper – a centralized coordination service used to maintain configuration information across cluster nodes in a distributed environment

The fundamental unit of data in Kafka is a message. A message in Kafka is represented as a key-value pair. Kafka converts all messages into byte arrays. It should be noted that communications between the producers, consumers, and clusters in Kafka use the TCP protocol. Each server in a Kafka cluster is known as a broker. You can scale Kafka horizontally simply by adding additional brokers to the cluster.

The following diagram illustrates the architectural components in Kafka – a high level view.

Apache FOUNDATION

A topic in Kafka represents a logical collection of messages. You can think of it as a feed or category to which a producer can publish messages. Incidentally, a Kafka broker contains one or more topics that are in turn divided into one or more partitions. A partition is defined as an ordered sequence of messages. Partitions are the key to the ability of Kafka to scale dynamically, as partitions are distributed across multiple brokers.

You can have one or more producers that push messages into a cluster at any given point of time. A producer in Kafka publishes messages into a particular topic, and a consumer subscribes to a topic to receive the messages.

Choosing between Kafka and RabbitMQ

Both Kafka and RabbitMQ are popular open source message brokers that have been in wide use for quite some time. When should you choose Kafka over RabbitMQ? The choice depends on a few factors.

RabbitMQ is a fast message broker written in Erlang. Its rich routing capabilities and ability to offer per message acknowledgments are strong reasons to use it. RabbitMQ also provides a user-friendly web interface that you can use to monitor your RabbitMQ server. Take a look at my article to learn how to work with RabbitMQ in .Net.  

However, when it comes to supporting large deployments, Kafka scales much better than RabbitMQ – all you need to do is add more partitions. It should also be noted that RabbitMQ clusters do not tolerate network partitions. If you plan on clustering RabbitMQ servers, you should instead use federations. You can read more about RabbitMQ clusters and network partitions here.

Kafka also clearly outshines RabbitMQ in performance. A single Kafka instance can handle 100K messages per second, versus closer to 20K messages per second for RabbitMQ. Kafka is also a good choice when you want to transmit messages at low latency to support batch consumers, assuming that the consumers could be either online or offline.

Building the Kafka producer and Kafka consumer

In this section we will examine how we can build a producer and consumer for use with Kafka. To do this, we will build two console applications in Visual Studio – one of them will represent the producer and the other the consumer. And we will need to install a Kafka provider for .Net in both the producer and the consumer application.

Incidentally, there are many providers available, but in this post we will be using kafka-net, a native C# client for Apache Kafka. You can install kafka-net via the NuGet package manager from within Visual Studio. You can follow this link to the kafka-net GitHub repository.

Here is the main method for our Kafka producer:

static void Main(string[] args)
{
string payload ="Welcome to Kafka!";
string topic ="IDGTestTopic";
Message msg = new Message(payload);
Uri uri = new Uri(“http://localhost:9092”);
var options = new KafkaOptions(uri);
var router = new BrokerRouter(options);
var client = new Producer(router);
client.SendMessageAsync(topic, new List<Message> { msg }).Wait();
Console.ReadLine();
}

And here is the code for our Kafka consumer:

static void Main(string[] args)
{
string topic ="IDGTestTopic";
Uri uri = new Uri(“http://localhost:9092”);
var options = new KafkaOptions(uri);
var router = new BrokerRouter(options);
var consumer = new Consumer(new ConsumerOptions(topic, router));
foreach (var message in consumer.Consume())
{
Console.WriteLine(Encoding.UTF8.GetString(message.Value));
}
Console.ReadLine();
}

Note that you should include the Kafka namespaces in both the producer and consumer applications as shown below.

using KafkaNet;
using KafkaNet.Model;
using KafkaNet.Protocol;

Finally, just run the producer (producer first) and then the consumer. And that’s it! You should see the message “Welcome to Kafka!” displayed in the consumer console window.

While we have many messaging systems available to choose from—RabbitMQ, MSMQ, IBM MQ Series, etc.—Kafka is ahead of the pack for dealing with large streams of data that can originate from many publishers. Kafka is often used for IoT applications and log aggregation and other use cases that require low latency and strong message delivery guarantees.

If your application needs a fast and scalable message broker, Kafka is a great choice. Stay tuned for more posts on Kafka in this blog.

Source: InfoWorld Big Data

What is machine learning? Software derived from data

You’ve probably encountered the term “machine learning” more than a few times lately. Often used interchangeably with artificial intelligence, machine learning is in fact a subset of AI, both of which can trace their roots to MIT in the late 1950s.

Machine learning is something you probably encounter every day, whether you know it or not. The Siri and Alexa voice assistants, Facebook’s and Microsoft’s facial recognition, Amazon and Netflix recommendations, the technology that keeps self-driving cars from crashing into things – all are a result of advances in machine learning.

While still nowhere near as complex as a human brain, systems based on machine learning have achieved some impressive feats, like defeating human challengers at chess, Jeopardy, Go, and Texas Hold ‘em.

Dismissed for decades as overhyped and unrealistic (the infamous ”AI winter”), both AI and machine learning have enjoyed a huge resurgence over the last few years, thanks to a number of technological breakthroughs, a massive explosion in cheap computing horsepower, and a bounty of data for machine learning models to chew on.

Self-taught software

So what is machine learning, exactly? Let’s start by noting what it is not: a conventional, hand-coded, human-programmed computing application.

Unlike traditional software, which is great at following instructions but terrible at improvising, machine learning systems essentially code themselves, developing their own instructions by generalizing from examples.

The classic example is image recognition. Show a machine learning system enough photos of dogs (labeled “dogs”), as well as pictures of cats, trees, babies, bananas, or any other object (labeled “not dogs”), and if the system is trained correctly it will eventually get good at identifying canines, without a human being ever telling it what a dog is supposed to look like.

The spam filter in your email program is a good example of machine learning in action. After being exposed to hundreds of millions of spam samples, as well as non-spam email, it has learned to identify the key characteristics of those nasty unwanted messages. It’s not perfect, but it’s usually pretty accurate.

Supervised vs. unsupervised learning

This kind of machine learning is called supervised learning, which means that someone exposed the machine learning algorithm to an enormous set of training data, examined its output, then continuously tweaked its settings until it produced the expected result when shown data it had not seen before. (This is analogous to clicking the “not spam” button in your inbox when the filter traps a legitimate message by accident. The more you do that, the more the accuracy of the filter should improve.)

The most common supervised learning tasks involve classification and prediction (i.e, “regression”). Spam detection and image recognition are both classification problems. Predicting stock prices is a classic example of a regression problem.

A second kind of machine learning is called unsupervised learning. This is where the system pores over vast amounts of data to learn what “normal” data looks like, so it can detect anomalies and hidden patterns. Unsupervised machine learning is useful when you don’t really know what you’re looking for, so you can’t train the system to find it.

Unsupervised machine learning systems can identify patterns in vast amounts of data many times faster than humans can, which is why banks use them to flag fraudulent transactions, marketers deploy them to identify customers with similar attributes, and security software employs them to detect hostile activity on a network.

Clustering and association rule learning are two examples of unsupervised learning algorithms. Clustering is the secret sauce behind customer segmentation, for example, while association rule learning is used for recommendation engines.

Limitations of machine learning

Because each machine learning system creates its own connections, how a particular one actually works can be a bit of a black box. You can’t always reverse engineer the process to discover why your system can distinguish between a Pekingese and a Persian. As long as it works, it doesn’t really matter.

But a machine learning system is only as good as the data it has been exposed to – the classic example of “garbage in, garbage out.” When poorly trained or exposed to an insufficient data set, a machine learning algorithm can produce results that are not only wrong but discriminatory.

HP got into trouble back in 2009 when facial recognition technology built into the webcam on an HP MediaSmart laptop was able to unable to detect the faces of African Americans. In June 2015, faulty algorithms in the Google Photos app mislabeled two black Americans as gorillas. 

Another dramatic example: Microsoft’s ill-fated Taybot, a March 2016 experiment to see if an AI system could emulate human conversation by learning from tweets. In less than a day, malicious Twitter trolls had turned Tay into a hate-speech-spewing chat bot from hell. Talk about corrupted training data.

A machine learning lexicon

But machine learning is really just the tip of the AI berg. Other terms closely associated with machine learning are neural networks, deep learning, and cognitive computing.

Neural network. A computer architecture designed to mimic the structure of neurons in our brains, with each artificial neuron (microcircuit) connecting to other neurons inside the system. Neural networks are arranged in layers, with neurons in one layer passing data to multiple neurons in the next layer, and so on, until eventually they reach the output layer. This final layer is where the neural network presents its best guesses as to, say, what that dog-shaped object was, along with a confidence score.

There are multiple types of neural networks for solving different types of problems. Networks with large numbers of layers are called “deep neural networks.” Neural nets are some of the most important tools used in machine learning scenarios, but not the only ones.

Deep learning. This is essentially machine learning on steroids, using multi-layered (deep) neural networks to arrive at decisions based on “imperfect” or incomplete information. The deep learning system DeepStack is what defeated 11 professional poker players last December, by constantly recomputing its strategy after each round of bets. 

Cognitive computing. This is the term favored by IBM, creators of Watson, the supercomputer that kicked humanity’s ass at Jeopardy in 2011. The difference between cognitive computing and artificial intelligence, in IBM’s view, is that instead of replacing human intelligence, cognitive computing is designed to augment it—enabling doctors to diagnose illnesses more accurately, financial managers to make smarter recommendations, lawyers to search caselaw more quickly, and so on.

This, of course, is an extremely superficial overview. Those who want to dive more deeply into the intricacies of AI and machine learning can start with this semi-wonky tutorial from the University of Washington’s Pedro Domingos, or this series of Medium posts from Adam Geitgey, as well as “What deep learning really means” by InfoWorld’s Martin Heller.

Despite all the hype about AI, it’s not an overstatement to say that machine learning and the technologies associated with it are changing the world as we know it. Best to learn about it now, before the machines become fully self-aware.

Source: InfoWorld Big Data

Equinix Collaborates With SAP

Equinix, Inc. has announced that it is offering direct and private access to the SAP® Cloud portfolio, including SAP HANA® Enterprise Cloud and SAP Cloud Platform, in multiple markets across the globe. Dedicated, private connections are available via Equinix Cloud Exchange™ and the SAP Cloud Peering service in the Equinix Amsterdam, Frankfurt, Los Angeles, New York, Silicon Valley, Sydney, Toronto and Washington, D.C. International Business Exchange™ (IBX®) data centers, with additional markets planned for later this year. Through this connectivity, enterprise customers benefit from high-performance and secure access to SAP cloud services as part of a hybrid or multi-cloud strategy.

“Equinix recognizes that enterprise cloud needs vary, and by aligning a company’s business requirements to the best cloud services, they can create a more agile, flexible and scalable IT infrastructure.  With more than 130 million cloud subscribers, SAP has a strong foothold in the enterprise market, and by providing these customers and more with dedicated connectivity to their SAP software environments simply, securely and cost-effectively from Equinix Cloud Exchange, we help customers connect and build a hybrid cloud solution that works for them,” said Charles Meyers, president of strategy, services and innovation, Equinix.

As cloud adoption continues to rise, so does the growth of multi-cloud deployments. In fact, according to the recent IDC CloudView survey, 85% of respondents are either currently using a multi-cloud strategy or plan to do so in the near-term.* Equinix Cloud Exchange, with direct access to multiple cloud services and platforms, such as the SAP Cloud portfolio, helps enterprise customers to expedite the development of hybrid and multi-cloud solutions across multiple locations, with the goal of gaining global scale, performance and security.

SAP Cloud Peering provides direct access inside the Equinix Cloud Exchange to help customers looking to reap the benefits of the SAP Cloud portfolio, with the control and predictability of a dedicated connection. Initially, access will be available for SAP HANA Enterprise Cloud and SAP Cloud Platform, which serve as SAP’s IaaS and PaaS solutions respectively. SAP and Equinix plan to make available SAP SuccessFactors®, SAP Hybris®, SAP Ariba®solutions and others in the near future.

“SAP joined the Equinix Cloud Exchange platform to address customer requirements for enterprise hybrid architecture in an environment that lends itself to the very highest levels of performance and reliability. With SAP’s traditional base of more than 300,000 software customers seeking ways to take the next step in a cloud-enabled world, SAP has established efficient capabilities to deliver on those requirements,” said Christoph Boehm senior vice president and head of Cloud Delivery Services, SAP.

SAP continues to gain traction in enterprise cloud adoption, with particular strength in APAC and EMEA. According to a recent 451 Research** note, SAP’s APAC cloud subscription and support revenue grew by 54%, while it rose by 35% in EMEA and by 27% in the Americas.  Access to these cloud-based services in Equinix’s global footprint of data centers will help drive adoption and reach of SAP cloud offerings.

Equinix offers the industry’s broadest choice in cloud service providers, including AWS, Microsoft Azure, Oracle, Google Cloud Platform, and other leading cloud providers such as SAP.  Equinix offers direct connections to many of these platforms via Equinix Cloud Exchange or Equinix Cross Connects. Equinix Cloud Exchange is an advanced interconnection solution that provides virtualized, private direct connections that bypass the Internet to provide better security and performance with a range of bandwidth options. It is currently available in 23 markets, globally.

*Source:  IDC CloudView Survey, April 2017. N=6084 worldwide respondents, weighted by country, industry and company size. 
**Source: 451 Research, “SAP hits Q4 and FY2016 targets as cloud subscription/support revenue jumps 31%,” February 1, 2017

Source: CloudStrategyMag

IBM speeds deep learning by using multiple servers

For everyone frustrated by how long it takes to train deep learning models, IBM has some good news: It has unveiled a way to automatically split deep-learning training jobs across multiple physical servers — not just individual GPUs, but whole systems with their own separate sets of GPUs.

Now the bad news: It’s available only in IBM’s PowerAI 4.0 software package, which runs exclusively on IBM’s own OpenPower hardware systems.

Distributed Deep Learning (DDL) doesn’t require developers to learn an entirely new deep learning framework. It repackages several common frameworks for machine learning: TensorFlow, Torch, Caffe, Chainer, and Theano. Deep learning projecs that use those frameworks can then run in parallel across multiple hardware nodes.

IBM claims the speedup gained by scaling across nodes is nearly linear. One benchmark, using the ResNet-101 and ImageNet-22K data sets, needed 16 days to complete on one IBM S822LC server. Spread across 64 such systems, the same benchmark concluded in seven hours, or 58 times faster.

IBM offers two ways to use DDL. One, you can shell out the cash for the servers it’s designed for, which sport two Nvidia Tesla P100 units each, at about $50,000 a head. Two, you can run the PowerAI software in a cloud instance provided by IBM partner Nimbix, for around $0.43 an hour.

One thing you can’t do, though, is run PowerAI on commodity Intel x86 systems. IBM has no plans to offer PowerAI on that platform, citing tight integration between PowerAI’s proprietary components and the OpenPower systems designed to support them. Most of the magic, IBM says, comes from a machine-to-machine software interconnection system that rides on top of whatever hardware fabric is available. Typically, that’s an InfiniBand link, although IBM claims it can also work on conventional gigabit Ethernet (still, IBM admits it won’t run anywhere nearly as fast).

It’s been possible to do deep-learning training on multiple systems in a cluster for some time now, although each framework tends to have its own set of solutions. With Caffe, for example, there’s the Parallel ML System or CaffeOnSpark. TensorFlow can also be distributed across multiple servers, but again any integration with other frameworks is something you’ll have to add by hand.

IBM’s claimed advantage is that it works with multiple frameworks and without as much heavy lifting needed to set things up. But those come at the cost of running on IBM’s own iron.

Source: InfoWorld Big Data

How to avoid big data analytics failures

Big data and analytics initiatives can be game-changing, giving you insights to help blow past the competition, generate new revenue sources, and better serve customers.

Big data and analytics initiatives can also be colossal failures, resulting in lots of wasted money and time—not to mention the loss of talented technology professionals who become fed up at frustrating management blunders.

How can you avoid big data failures? Some of the best practices are the obvious ones from a basic business management standpoint: be sure to have executive buy-in from the most senior levels of the company, ensure adequate funding for all the technology investments that will be needed, and bring in the needed expertise and/or having good training in place. If you don’t address these basics first, nothing else really matters.

But assuming that you have done the basics, what separates success from failure in big data analytics is how you deal with the technical issues and challenges of big data analytics. Here’s what you can do to stay on the success side of the equation.

Source: InfoWorld Big Data

ZNetLive Rolls Out Managed Microsoft Azure Stack

ZNetLive has announced that it has made available Microsoft Azure Stack — Microsoft’s truly consistent platform for hybrid cloud, to businesses of all sizes with complete deployment and operational support. This will enable enterprises to seamlessly implement and manage their data in a hybrid cloud environment, while realizing its full potential with benefits like agility, scalability, and flexibility, irrespective of their size or cloud expertise of their IT staff.

Azure Stack provides a secure way to enterprises who wish to use Azure public cloud but reasons like data criticality, compliance and data accessibility prevent them from doing so. An extension of Microsoft Azure platform, it helps them to implement same capabilities that Azure public cloud offers, within their own data centers, on premise. It provides them a secure way to have control over their data that’s safe within their boundaries, behind all their security software. This helps them get the benefits of both the public and private cloud worlds.

Microsoft announced the Azure Stack’s ready to order availability in the recently concluded Microsoft partner event – Microsoft Inspire. “We have delivered Azure Stack software to our hardware partners, enabling us to begin the certification process for their integrated systems, with the first systems to begin shipping in September,” wrote Mike Neil, corporate vice president, Azure Infrastructure and Management, in his blog post.

“Azure Stack is another step by Microsoft in fulfilling its Digital Transformation goals. ZNetLive has always been among the fore-runners in bringing the Digital transformation technologies to the end customers and thus, we decided to offer dedicated support services for Microsoft Azure Stack, while attending Microsoft Inspire in Washington D.C, itself. With Microsoft, we take our next step in creating a digitally transformed world,” said Munesh Jadoun, founder & CEO, ZNetLive. 

ZNetLive’s Microsoft Azure Stack management services will provide benefits, including but not limited to these mentioned below:

One support stop for Azure – ZNetLive will provide completely unified support for Azure cloud and Azure Stack cloud including platform elements like hardware, VMs etc. ZNetLive’s Microsoft certified Azure experts will provide round the clock assistance in creating, installing, operating, monitoring and optimizing cloud environments using Azure Stack. 

Assurance and trust with expertise of handling Microsoft Cloud services – Being the first Microsoft Cloud Solution Provider (CSP) in Rajasthan, India, ZNetLive has been working closely with Microsoft for over a decade and has been providing Microsoft cloud management services to benefit the end customers by getting them digitally transformed.  

As its long-standing partner, it has earned many Microsoft partnerships like Cloud OS Network Partner, Gold Hosting, Gold Data Center, Gold Cloud Productivity, just to name a few.

Ensuring secure services – With managed Microsoft Azure Stack, the customers can select state of the art, certified data centers of ZNetLive for hosting their Azure Stack cloud. ZNetLive trained technical experts will take care of the application and infrastructure security with regular health check monitoring and recommending security services to help customers meet regulatory compliances.

 “We have been working with Microsoft Azure for a long time now. Before Microsoft Azure Stack, we used System Center 2012 R2 product suite to create private cloud environments for our customers. This led to an increase in costs due to hardware, licensing, maintenance, upgrades etc.

But now with Azure Stack, we’ll be able to provide our customers the same Azure capabilities in their own data centers at much lesser prices. Since the intuitive interface of Azure Stack is same as that of Azure, the team will be at ease creating VMs, cloud databases, and other Azure cloud services with no additional training,” said Bhupender Singh, chief technical officer, ZNetLive. 

Source: CloudStrategyMag

Report: NetOps And Devops Want More Collaboration In A Multi-Cloud World

F5 Networks has announced the results of a recent survey comparing the views of over 850 NetOps and DevOps IT professionals on their respective disciplines and collaboration practices. Traditionally, the larger IT market has viewed these two groups as somewhat antagonistic toward one other. However, the F5 survey indicates they are largely aligned on priorities, with converging interests around the production pipeline and automation capabilities. Reconciling survey results with the current trend of DevOps turning to outside solutions (such as shadow IT) to deploy applications, an implication emerges that NetOps will need additional skills to adequately support efforts tied to digital transformation and multi-cloud deployments.

In parallel, F5’s Americas Agility conference takes place this week in Chicago, featuring a dedicated focus toward topics relevant to the interplay between NetOps and DevOps. With hands-on experiences such as technology labs and training classes aimed at helping operations and development professionals take advantage of programmable solutions, the event explores how modern applications are successfully developed, deployed, secured, and supported.

Key Survey Findings

NetOps and DevOps respect each other’s priorities: Within each group, over three-quarters of NetOps and DevOps personnel believe the other function to be prioritizing “the right things” within IT, signaling a common understanding of broader goals, and opportunities to increase collaboration between the teams. In addition, the groups are fairly aligned on the pace that apps and services are delivered, with frequency of deployments satisfying a significant majority of both DevOps (70%) and NetOps (74%) personnel.

Support for automation: Both segments agreed that automation within the production pipeline is important, with an average rating of significance on a 5-point scale of 4.0 from DevOps and 3.5 from NetOps. Respondents also reported more confidence in the reliability, performance, and security of applications when the production pipeline is more than 50% automated.

Dissonance around pipeline access: A difference of opinion surfaced around the ideal level of shared access to production resources. Forty-five percent of DevOps believe they should have access to at least 75% of the production pipeline, with significantly less (31%) of NetOps respondents placing the access figure for DevOps that high, hinting at a partial disconnect surrounding expectations and best practices within IT. This misalignment can hamper efforts to streamline processes and deliver applications the business needs to succeed in a digital economy.

Differences driving multi-cloud deployments: The majority of DevOps (65%) admitted to being influenced toward adopting cloud solutions either “a lot” or “some” by the state of access to the pipeline via automation/self-service capabilities. Related, a significant portion of NetOps (44%) indicated that DevOps’ use of outside cloud technologies affects their desire to provide pipeline access “some,” with an additional 21% stating that it influences them “a lot.” One result of this is the use of multiple cloud solutions and providers across IT, further complicating the process of delivering, deploying, and scaling applications that support digital transformation efforts.

“We see some interesting data points around network- and development-focused personnel,” said Ben Gibson, EVP and chief marketing officer, F5. “While DevOps seeks more open access to the deployment pipeline to drive the speed of innovation, NetOps can be much more cautious around permissions — presumably because they’re the ones that bear the responsibility if security, availability, or performance are compromised. Despite different approaches, both groups support each other’s efforts, and seem to agree that more flexible technologies are needed to overcome current business limitations, bridge disparate functions, and position IT to better leverage public, private, and multi-cloud environments. Overall, neither group’s responses seemed particularly well aligned with the ‘us vs. them’ narrative that has loomed large in the media to date.”

Bridging IT Functions between NetOps and DevOps

Taken together, the survey results point to a rising interest in automation and self-service that can be linked to the rapid adoption of cloud-based solutions, and the desired flexibility they provide. NetOps and DevOps each demonstrate a willingness to introduce emerging technologies and methods into the production pipeline. However, the speed of innovation can also push traditional IT operations teams beyond their current skill levels, contributing potential resistance on the path to streamlined future application rollouts. From the survey, DevOps reports a confidence level of 3.6 on a 5-point scale in terms of if they have the skills their job function requires, with NetOps’ self-assessment yielding a slightly lower figure (3.4).

The survey findings are in step with F5’s belief that enhanced education will play a larger role in bringing these two groups together and rallying around shared goals. To that aim, F5 offers a growing library of industry certification programs that help customers tailor their application delivery infrastructures across related disciplines and provide common frameworks for different roles throughout the organization. With testing available at F5’s Agility conferences and other venues, over 2,500 certifications have been earned in the past year. In addition, F5’s vibrant DevCentral community provides a means for over 250,000 customers, developers, and other IT professionals to pool their collective knowledge, learn from each other’s experiences, and make the most of their technology investments.

Looking forward, F5 is focused on enabling shared empowerment between NetOps and DevOps teammates, concurrent with their use of multi-cloud solutions. The company’s programmable BIG-IP® products, along with adjacent technologies such as its container-focused offerings, provide compelling platforms for evolving IT groups to apply valuable acceleration, availability, and security services to make their applications, users, and operations practices more successful. Further detail on the survey results and methodology can be found in a companion report.

Source: CloudStrategyMag

LockPath Included In Gartner’s 2017 Magic Quadrant For BCMP Solutions

LockPath has announced it has been included in Gartner Inc.’s Magic Quadrant for Business Continuity Management Program Solutions, Worldwide.

LockPath was one of 12 vendors included in Gartner Inc.’s report, which was published July 12, by Gartner analysts Roberta Witty and Mark Jaggers. The report, which aims to help organizations evaluate business continuity management program (BCMP) software solutions, recognized LockPath as a challenger in the space for its Keylight Platform.

According to Witty and Jaggers, “The 2017 BCMP solutions market — with an estimated $300 million global market revenue — has broadened its IT disaster recovery management, crisis management and risk management capabilities since 2016.”

LockPath’s Keylight Platform supports enterprise BCMP efforts to identify and mitigate operational risks that could potentially lead to disruption. The platform’s integrated and holistic approach to risk management allows organizations to coordinate efforts across the business to continue operations after serious incidents or disasters.

“We are thrilled to be included in this year’s Magic Quadrant,” said Chris Caldwell, CEO, LockPath. “With the number of threats that can adversely impact operations multiplying, our customers are finding value in including business continuity as part of their overall integrated risk management and GRC programs.”

Source: CloudStrategyMag