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CIOs should lean on AI ‘giants’ for machine learning strategy

NEW YORK — Machine learning and deep learning will be part of every data science organization, according to Edd Wilder-James, former vice president of technology strategy at Silicon Valley Data Science and now an open source strategist at Google’s TensorFlow.

Wilder-James, who spoke at the Strata Data Conference, pointed to recent advancements in image and speech recognition algorithms as examples of why machine learning and deep learning are going mainstream. He believes image and speech recognition software has evolved to the point where it can see and understand some things as well as — and in some use cases better than — humans. That makes it ripe to become part of the internal workings of applications and the driver of new and better services to internal and external customers, he said.

But what investments in AI should CIOs make to provide these capabilities to their companies? When building a machine learning strategy, choice abounds, Wilder-James said.

Machine learning vs. deep learning

Deep learning is a subset of machine learning, but it’s different enough to be discussed separately, according to Wilder-James. Examples of machine learning models include optimization, fraud detection and preventive maintenance. “We use machine learning to identify patterns,” Wilder-James said. “Here’s a pattern. Now, what do we know? What can we do as a result of identifying this pattern? Can we take action?”

Deep learning models perform tasks that more closely resemble human intelligence such as image processing and recognition. “With a massive amount of compute power, we’re able to look at a massively large number of input signals,” Wilder-James said. “And, so what a computer is able to do starts to look like human cognitive abilities.”

Some of the terrain for machine learning will look familiar to CIOs. Statistical programming languages such as SAS, SPSS and Matlab are known territory for IT departments. Open source counterparts such as R, Python and Spark are also machine-learning ready. “Open source is probably a better guarantee of stability and a good choice to make in terms of avoiding lock-in and ensuring you have support,” Wilder-James said.

Unlike other tech rollouts

The rollout of machine learning and deep learning models, however, is a different process than most technology rollouts. After getting a handle on the problem, CIOs will need to investigate if machine learning is even an appropriate solution.

“It may not be true that you can solve it with machine learning,” Wilder-James said. “This is one important difference from other technical rollouts. You don’t know if you’ll be successful or not. You have to enter into this on the pilot, proof-of-concept ladder.”

The most time-consuming step in deploying a machine learning model is feature engineering, or finding features in the data that will help the algorithms self-tune. Deep learning models skip the tedious feature engineering step and go right to the training step. To tune a deep learning model correctly requires immense data sets, graphic processing units or tensor processing units, and time. Wilder-James said it could take weeks and even months to train a deep learning model.

One more thing to note: Building deep learning models is hard and won’t be a part of most companies’ machine learning strategy.

“You have to be aware that a lot of what’s coming out is the closest to research IT has ever been,” he said. “These things are being published in papers and deployed in production in very short cycles.”

CIOs whose companies are not inclined to invest heavily in AI research and development should instead rely on prebuilt, reusable machine and deep learning models rather than reinvent the wheel. Image recognition models, such as Inception, and natural language models, such as SyntaxNet and Parsey McParseface, are examples of models that are ready and available for use.

“You can stand on the shoulders of giants, I guess that’s what I’m trying to say,” Wilder-James said. “It doesn’t have to be from scratch.”

Machine learning tech

The good news for CIOs is that vendors have set the stage to start building a machine learning strategy now. TensorFlow, a machine learning software library, is one of the best known toolkits out there. “It’s got the buzz because it’s an open source project out of Google,” Wilder-James said. “It runs fast and is ubiquitous.”

While not terribly developer-friendly, a simplified interface called Keras eases the burden and can handle the majority of use cases. And TensorFlow isn’t the only deep learning library or framework option, either. Others include MXNet, PyTorch, CNTK, and Deeplearning4j.

For CIOs who want AI to live on premises, technologies such as Nvidia’s DGX-1 box, which retails for $129,000, are available.

But CIOs can also utilize cloud as a computing resource, which would cost anywhere between $5 and $15 an hour, according to Wilder-James. “I worked it out, and the cloud cost is roughly the same as running the physical machine continuously for about a year,” he said.

Or they can choose to go the hosted platform route, where a service provider will run trained models for a company. And other tools, such as domain-specific proprietary tools like the personalization platform from Nara Logics, can fill out the AI infrastructure.

“It’s the same kind of range we have with plenty of other services out there,” he said. “Do you rent an EC2 instance to run a database or do you subscribe to Amazon Redshift? You can pick the level of abstraction that you want for these services.”

Still, before investments in technology and talent are made, a machine learning strategy should start with the basics: “The single best thing you can do to prepare with AI in the future is to develop a competency with your own data, whether it’s getting access to data, integrating data out of silos, providing data results readily to employees,” Wilder-James said. “Understanding how to get at your data is going to be the thing to prepare you best.”

AWS and Microsoft announce Gluon, making deep learning accessible to all developers – News Center

New open source deep learning interface allows developers to more easily and quickly build machine learning models without compromising training performance. Jointly developed reference specification makes it possible for Gluon to work with any deep learning engine; support for Apache MXNet available today and support for Microsoft Cognitive Toolkit coming soon.

SEATTLE and REDMOND, Wash. — Oct. 12, 2017 — On Thursday, Amazon Web Services Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), and Microsoft Corp. (NASDAQ: MSFT) announced a new deep learning library, called Gluon, that allows developers of all skill levels to prototype, build, train and deploy sophisticated machine learning models for the cloud, devices at the edge and mobile apps. The Gluon interface currently works with Apache MXNet and will support Microsoft Cognitive Toolkit (CNTK) in an upcoming release. With the Gluon interface, developers can build machine learning models using a simple Python API and a range of prebuilt, optimized neural network components. This makes it easier for developers of all skill levels to build neural networks using simple, concise code, without sacrificing performance. AWS and Microsoft published Gluon’s reference specification so other deep learning engines can be integrated with the interface. To get started with the Gluon interface, visit https://github.com/gluon-api/gluon-api/.

Developers build neural networks using three components: training data, a model and an algorithm. The algorithm trains the model to understand patterns in the data. Because the volume of data is large and the models and algorithms are complex, training a model often takes days or even weeks. Deep learning engines like Apache MXNet, Microsoft Cognitive Toolkit and TensorFlow have emerged to help optimize and speed the training process. However, these engines require developers to define the models and algorithms up front using lengthy, complex code that is difficult to change. Other deep learning tools make model-building easier, but this simplicity can come at the cost of slower training performance.

The Gluon interface gives developers the best of both worlds — a concise, easy-to-understand programming interface that enables developers to quickly prototype and experiment with neural network models, and a training method that has minimal impact on the speed of the underlying engine. Developers can use the Gluon interface to create neural networks on the fly, and to change their size and shape dynamically. In addition, because the Gluon interface brings together the training algorithm and the neural network model, developers can perform model training one step at a time. This means it is much easier to debug, update and reuse neural networks.

“The potential of machine learning can only be realized if it is accessible to all developers. Today’s reality is that building and training machine learning models require a great deal of heavy lifting and specialized expertise,” said Swami Sivasubramanian, VP of Amazon AI. “We created the Gluon interface so building neural networks and training models can be as easy as building an app. We look forward to our collaboration with Microsoft on continuing to evolve the Gluon interface for developers interested in making machine learning easier to use.”

“We believe it is important for the industry to work together and pool resources to build technology that benefits the broader community,” said Eric Boyd, corporate vice president of Microsoft AI and Research. “This is why Microsoft has collaborated with AWS to create the Gluon interface and enable an open AI ecosystem where developers have freedom of choice. Machine learning has the ability to transform the way we work, interact and communicate. To make this happen we need to put the right tools in the right hands, and the Gluon interface is a step in this direction.”

“FINRA is using deep learning tools to process the vast amount of data we collect in our data lake,” said Saman Michael Far, senior vice president and CTO, FINRA. “We are excited about the new Gluon interface, which makes it easier to leverage the capabilities of Apache MXNet, an open source framework that aligns with FINRA’s strategy of embracing open source and cloud for machine learning on big data.”

“I rarely see software engineering abstraction principles and numerical machine learning playing well together — and something that may look good in a tutorial could be hundreds of lines of code,” said Andrew Moore, dean of the School of Computer Science at Carnegie Mellon University. “I really appreciate how the Gluon interface is able to keep the code complexity at the same level as the concept; it’s a welcome addition to the machine learning community.”

“The Gluon interface solves the age old problem of having to choose between ease of use and performance, and I know it will resonate with my students,” said Nikolaos Vasiloglou, adjunct professor of Electrical Engineering and Computer Science at Georgia Institute of Technology. “The Gluon interface dramatically accelerates the pace at which students can pick up, apply and innovate on new applications of machine learning. The documentation is great, and I’m looking forward to teaching it as part of my computer science course and in seminars that focus on teaching cutting-edge machine learning concepts across different cities in the U.S.”

“We think the Gluon interface will be an important addition to our machine learning toolkit because it makes it easy to prototype machine learning models,” said Takero Ibuki, senior research engineer at DOCOMO Innovations. “The efficiency and flexibility this interface provides will enable our teams to be more agile and experiment in ways that would have required a prohibitive time investment in the past.”

The Gluon interface is open source and available today in Apache MXNet 0.11, with support for CNTK in an upcoming release. Developers can learn how to get started using Gluon with MXNet by viewing tutorials for both beginners and experts available by visiting https://mxnet.incubator.apache.org/gluon/.

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ManageEngine launches OpManager Plus deep packet inspection tool

ManageEngine released its deep packet inspection tool, OpManager Plus. The deep packet inspection tool includes features such as bandwidth monitoring and captures packets from network flows to help engineers assess the causes of network bottlenecks or unusual traffic activity.

OpManager Plus is both a deep packet inspection tool and a network management platform, aimed at improving the ways that service providers and enterprises manage their IT infrastructure. The offering comes pre-equipped with discovery rules that can be reconfigured for different tasks, a set of alert engines and a collection of templates designed to help IT teams to set up a monitoring system. The offering is able to support network monitoring and tracking for virtualized systems, databases and enterprise applications. It can also be used to support configuration management and IP address management.

OpManager Plus monitors bandwidth using Simple Network Management Protocol in combination with network flows, alongside packet inspection. Through use of the app, engineers can determine if performance bottlenecks stem from the network or the application, ManageEngine said.

“Network teams rely on a set of tools to claim that the issue is on the app side, while the app team will blame it on the network. An integrated tool that gives visibility into network and application performance can help both teams identify what’s really causing the issue,” said Dev Anand, director of product management at ManageEngine, in a statement.

OpManager is priced at $4,995.

Nyansa expands analytics capabilities in Voyance

Nyansa Inc. has added analytics capabilities and a client troubleshooting dashboard to Voyance, the company’s network analysis tool.

The new features, launched this week, aim to provide better support to IT staff when they are resolving client network issues.

The analytics capabilities track and measure every client network transaction in real time, allowing IT staffs to distinguish between client versus network-wide issues at the time of an incident.

Other capabilities added include access to a client device’s timeline, summarized views of the root causes of network incidents, and the ability search for a client based on username, hostname or MAC address.

The Voyance network analysis tool is based on a combination of deep packet inspection and cloud-based analytics. It sends all collected network data to servers hosted by Amazon Web Services.

The data is then inspected and retransmitted to the user’s location, where it can be evaluated by IT staff via an easy-to-understand user display.

The network analysis tool is available through one-, three- or five-year subscriptions. Customers can run the software on a dedicated appliance on site or as a virtual machine within an AWS or Microsoft Azure deployment.

Nyansa released Voyance in April 2016. Its big-name customers include Uber, Netflix and Tesla Motors.

Aerohive premieres new access point

Aerohive Networks introduced a combined access point and switch, with capabilities embedded to support IoT. The vendor said that the AP150W can be installed in minutes either placed on a desktop or via an Ethernet-jack wall mounting. The new device supports 802.11ac Wave 2 connectivity.

The new device supports ZigBee and Bluetooth Low Energy, as well as Gigabit Ethernet switching. It can be used to power a variety of components through integrated Power over Ethernet and pass through ports, allowing it to be used in existing cabling and switch infrastructure.

The AP150W is available in September, priced at $299. The cost includes a subscription to Aerohive’s cloud-based Connect management app.

“By packing 802.11ac Wave 2 Wi-Fi, Gigabit Ethernet switching, Bluetooth Low Energy and ZigBee technologies into a small form factor…Wi-Fi in every room has finally become affordable and easy,” said Alan Amrod, Aerohive’s senior vice president of products, in a statement.

IBM cracks the code for speeding up its deep learning platform

Graphics processing units are a natural fit for deep learning because they can crunch through large amounts of data quickly, which is important when training data-hungry models.

But GPUs have one catch. Adding more GPUs to a deep learning platform doesn’t necessarily lead to faster results. While individual GPUs process data quickly, they can be slow to communicate their computations to other GPUs, which has limited the degree to which users can take advantage of multiple servers to parallelize jobs and put a cap on the scalability of deep learning models.

IBM recently took on this problem to improve scalability in deep learning and wrote code for its deep learning platform to improve communication between GPUs.

“The rate at which [GPUs] update each other significantly affects your ability to scale deep learning,” said Hillery Hunter, director of systems acceleration and memory at IBM. “We feel like deep learning has been held back because of these long wait times.”

Hunter’s team wrote new software and algorithms to optimize communication between GPUs spread across multiple servers. The team used the algorithm to train an image-recognition neural network on 7.5 million images from the ImageNet-22k data set in seven hours. This is a new speed record for training neural networks on the image data set, breaking the previous mark of 10 days, which was held by Microsoft, IBM said.

Hunter said it’s essential to speed up training times in deep learning projects. Unlike virtually every other area of computing today, training deep learning models can take days, which might discourage more casual users.

“We feel it’s necessary to bring the wait times down,” Hunter said.

IBM is rolling out the new functionality in its PowerAI software, a deep learning platform that pulls together and configures popular open source machine learning software, including Caffe, Torch and Tensorflow. PowerAI is available on IBM’s Power Systems line of servers.

But the main reason to take note of the news, according to Forrester analyst Mike Gualtieri, is the GPU optimization software might bring new functionality to existing tools — namely Watson.

“I think the main significance of this is that IBM can bring deep learning to Watson,” he said.

Watson currently has API connectors for users to do deep learning in specific areas, including translation, speech to text and text to speech. But its deep learning offerings are prescribed. By opening up Watson to open source deep learning platforms, its strength in answering natural-language queries could be applied to deeper questions.