Machine Learning

Perspectives from Microsoft Data Scientists Val Fontama and Wee Hyong Tok

Fri, 09 Jan 2015 17:00:00 GMT

Repost of an article earlier published on the SQL Data Platform Insider blog.

In an earlier post we talked about a new book titled Predictive Analytics with Microsoft Azure Machine Learning which released in December on Amazon.com where it was doing rather well.

The Data Platform Insider blog team at SQL recently had an opportunity to sit down with a couple of Microsoft authors of that book to learn more about their roles as Data Scientists, some of the real-world successes they’ve seen, their perspectives on opportunities in this evolving field as well as about their new book. Click here to hear directly from authors Val and Wee Hyong.

ML Blog Team

Azure ML Predicts Customers’ Shopping Lists – Even Before They Shop!

Thu, 08 Jan 2015 17:00:00 GMT

We continue our series of posts how Microsoft customers are gaining actionable insights on their data by operationalizing ML and advanced analytics – at scale and in the cloud.

As one of the largest independent food delivery service companies in the UK, JJ Food Service provides over 60,000 customers with everything they need for their own food businesses. Their catalog has over 4,500 products ranging from fresh, frozen or dry foods to paper and cleaning supplies and get fulfilled from any of eight warehouses.

Customers can either place orders online or by speaking to call center representatives over the phone. As orders come in each day, logistics teams route and sequence these orders, employees at warehouses then load the appropriate products overnight, and drivers hit their delivery routes the next morning – and the same cycle repeats all over again.

Although the existing processes at JJ Food Service are quite streamlined, as a company that prides itself on staying at the cutting-edge of technology, their ambitions ran much further.

Back in 2004, JJ Food Service implemented Microsoft Dynamics for their ERP and CRM needs. Over the past decade, they refined their operations and Microsoft Dynamics AX now powers their entire operations – right from HR, procurement and sales to warehouse management and order processing.

Recognizing that they had an exceptionally rich vein of customer data, the Chief Operating Officer at JJ Food Service, Mushtaque Ahmed, saw an opportunity to use this data to further boost customer satisfaction. An area where they felt they could save their customers’ some time was by anticipating customer orders, i.e. recommending products to them even before they had entered anything into the system. They had several other ideas for predictive analytics too. At the same time, the company was concerned about the potentially big costs they might incur in staffing up and implementing an advanced analytics project such as this.

That’s when Azure ML entered the picture.

Predictive Shopping Lists

Customer orders at JJ Food Service, of course, vary widely in terms of what gets purchased and when, order size, type, frequency and many other criteria. In anticipating customers’ future needs, what they needed were tailored insights based on each customer’s past order patterns. For instance, a particular restaurant might order salad greens every day, flour about every two weeks, and cooking oil once a month. “To be successful, we needed to be relevant for that week, that day, that exact point in time,” Ahmed explained.

JJ Food Service was convinced that Azure ML could help them address their needs in a very cost-effective manner. They started working with the Microsoft Azure team, first writing code for their website to capture customer behavior and then using three years of transactional data to train an Azure ML predictive model. Next, they integrated the recommendations from this model into both their call center environment and their website, thus ensuring that their phone-based customers would get the exact same recommendations (via call center representatives) as what online customers would see on their site.

The system took only three months to implement. Today, whether customers call in or log in, the system bubbles up the same predictions using its analysis of past purchases – in both cases, the order pad gets filled out in the same fashion, and automatically.

The net result? More satisfied customers who find a high level of efficiency in their shopping experience.

Recommendations Add a More Personal Touch

In addition to the predictive shopping list, customers also get recommendations for related items that they might want to order. For instance, if a fish and chips shop were to order batter, the system might ask whether they need specific spices that go along with that. Also, prior to checkout, the system reviews the overall order to determine whether the combination of items shopped indicates a need for additional products. For instance, if a fast-food restaurant orders meat, poultry, vegetables and beverages, would it also need cooking oil? Or perhaps paper cups, if their supply might be running low?

JJ Food Service estimates that these recommendations currently make up about 5% of the shopping cart. While that may not seem large – and, in fact, Ahmed expects this number to go down a bit as the system gets smarter at predicting orders even more accurately – when you consider the company’s size, this really adds up. Plus it’s a nice personal touch for customers. As Ahmed says, “The wow factor is huge. Customers are amazed that we can predict so accurately what they need.”

Targeting New Customers More Effectively

JJ Food Service realizes that there’s no better way to capture business from new customers than by making themselves indispensable from the very moment they log on.

By using the Azure ML recommendation system to display products purchased by similar companies, they are now able to show immediate value to brand new customers, shaving valuable time that would otherwise be spent in browsing a new catalog and compiling orders for the first time.

At JJ Food Service this is just the start of a journey. They are looking at additional possibilities beyond increasing customer satisfaction and driving incremental sales. For instance, they plan to stock their warehouses more efficiently by using forecasts of what customers, in aggregate, are likely to buy in the near future. They are also exploring how to use the recommendation system for promotions and to target new product launches at specific types of customers.

As Ahmed concludes. “Microsoft Dynamics AX works hard for us, automating processes. But we also need to make these processes intelligent – and that’s where Microsoft Azure Machine Learning is vital.”

ML Blog Team
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Readers’ Choice – Our 10 Most Popular ML Blog Posts of 2014

Tue, 30 Dec 2014 18:15:00 GMT

We launched this blog in June 2014 with the intent of sharing important advances and practical knowledge accumulated by Microsoft in the field of ML. After six months of regular posts, many of them authored by world-leading ML researchers and practitioners, we are seeing tens of thousands of readers such as yourself regularly visiting our blog site where, we hope, you are finding articles of value and relevance to your own ML journeys.

As we take one final look back at the year 2014, we figured we would share the top 10 most-read posts of 2014. Here they are, listed below, in increasing order of popularity.

10. Machine Learning, meet Computer Vision
Jamie Shotton, Antonio Criminisi and Sebastian Nowozin explore the challenges of computer vision and touch on the powerful ML technique of decision forests for pixel-wise classification.

9. Python Tools for Visual Studio now integrates with Azure Machine Learning
Shahrokh Mortazavi talks about Python support in Azure ML, including the powerful Python centric Data Science IDE, PTVS – a completely free and open source tool that is helping democratize ML and advanced analytics.

8. Vowpal Wabbit for Fast Learning
John Langford shares information about the speedy VW open source ML system sponsored by Microsoft.

7. Machine Learning and Text Analytics
Dr. Ashok Chandra talks about how we are now able to take advantage of signals to determine the salient entities being discussed in textual articles.

6. The Joy (and Hard Work) of Machine Learning
Joseph Sirosh discusses how enterprises can tap into the potential of ML to deliver enormous value in diverse applications that can improve customer experience, reduce the risk of systemic failures, grow revenue and bring about significant cost savings.

5. Machine Learning Trends from NIPS 2014
John Platt shares 3 exciting trends he saw at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal this year.

4. What is Machine Learning?
John Platt provides some much-needed context around ML and also shares a taxonomy of ML applications.

3. Twenty Years of Machine Learning at Microsoft
John Platt discusses Microsoft’s 20+ years of experience in creating ML systems and applying them to real world problems, including what it takes to actually deploy ML in production.

2. How Azure ML Partners are Innovating for their Customers
At the Worldwide Partner Conference, Joseph Sirosh talks about how Azure ML – which is changing the game for building ML applications at scale and in the cloud – is being used by Microsoft’s partners to rapidly build novel solutions for our customers.

1. Rapid Progress in Automatic Image Captioning
John Platt talks about the exciting progress researchers have made in creating systems to automatically generate descriptive captions of images.

We wish our readers a very happy and productive 2015!

ML Blog Team

Skype Translator Puts Machine Learning to the Test

Thu, 25 Dec 2014 14:00:00 GMT

Our previous post had a video showing Skype's automatic speech translation in action. In this post, we share an infographic from the Skype team about how they perform such automatic speech recognition and translation (including how they translates instant messages in over 40 languages).

You can register for a preview of the Skype Translator here.

ML Blog Team

WIRED: How Skype Used AI to Build Its Amazing New Language Translator

Tue, 23 Dec 2014 17:10:00 GMT

Re-posted from an article that recently appeared on

“… a new Microsoft technology that seems borrowed from the world of Star Trek”

“… a Skype add-on that listens to the English words you speak into Microsoft’s internet phone-calling software and translates them into Spanish, or vice versa.”

“… an amazing technology, and it’s based on work that’s been going on quietly inside Microsoft’s research and development labs for more than a decade.”

Read the original WIRED magazine post here.

ML Blog Team

Azure Data Factory Now Integrates with Azure ML!

Wed, 17 Dec 2014 16:30:00 GMT

An update to Azure Data Factory (ADF) now integrates this service with Azure Machine Learning, allowing you to run finished Azure ML models from within ADF pipelines.

Click on this link or the image below for more details on how to take advantage of this feature. You can also visit the Azure Data Factory GitHub repository where there’s an end to end Twitter analytics sample that takes advantage of this new integration capability.

ML Blog Team

Machine Learning Trends from NIPS 2014

Tue, 16 Dec 2014 14:00:00 GMT

This blog post is authored by John Platt, Deputy Managing Director and Distinguished Scientist at Microsoft Research.

I just returned from the Neural Information Processing Systems (NIPS) 2014 conference, which was held this year in Montreal, Canada. NIPS is one of the two main machine learning (ML) conferences, the other being ICML.

NIPS has broad coverage of many ML sub-fields, including links to neuroscience (hence the name). I thought that the program chairs and committee created a conference which appealed to many different ML specialists – excellent job!

I want to share three exciting trends that I saw in NIPS this year:

Continued rapid progress in deep learning and neural networks
Making large-scale learning more practical
Research into constraints that arise in the real practice of ML

Deep Learning

Deep learning is the automatic construction of deep models from data. They are called “deep” because the models compute desired functions in multiple steps, rather than trying to solve problems in one or two steps. Deep learning is typically accomplished using neural networks, which are models that use matrix multiplication and non-linearities to build their functions.

Progress in deep learning since 2011 has been amazingly rapid. For example, on a benchmark of recognizing objects in images, the error rate has decreased 40% relative, per year. Deep learning has also become more broadly applicable than just classifying images.

One challenging problem in ML is the co-estimation of outputs that are strongly coupled. For example, when translating a sentence from one language to another, you don’t want to translate word-by-word. You have to think about the entire sentence you would produce.

Previously, when ML algorithms estimated coupled outputs, they would explicitly use inference, which can be slow at run time. Recently, there’s been some exciting work of having neural networks do the inference implicitly. At NIPS, Ilya Sutskever showed that you can use a deep LSTM model to do machine translation (MT) and perform almost as well as the state-of-the-art MT system. Ilya’s system is more general: it can map input sequences to output sequences. At NIPS, there was also other work in coupling outputs across large amounts of space or time. For example, Jason Weston had a workshop paper that had a neural network that used a content-addressable memory to perform question answering. The “Neural Turing Machine” uses a similar idea.

Given the successes of deep learning, researchers are trying to understand how they work. Ba and Caruana had a NIPS paper which showed that, once a deep network is trained, a shallow network can learn the same function from the outputs of the deep network. The shallow network can’t learn the same function directly from the data. This indicates that deep learning could be an optimization/learning trick.

Many people (including us!) have used middle layers in deep neural networks as feature detectors in related tasks. There was a wonderful talk at NIPS, where the authors did a set of careful experiments that examined this pattern. They trained a deep network on one set of 500 visual categories, kept the first N layers, and then retrained on a different set of 500. They found that, if you use middle layers and retrain on top, you lose some accuracy due to the sub-optimality of training. They found that if you use the highest layers, you lose some accuracy due to the features being too specific. Fine tuning everything recovers all lost accuracy. Very useful to know.

Large-Scale Training

Large-scale training (of all sorts of models) has continued to be an interesting research vein. While not that many people have training sets above 1TB, the models that use that scale data tend to be commercially very valuable.

Training in machine learning is a form of parameter optimization: an ML model can be viewed as having a set of knobs that are adjusted to make the model perform well on a training set. Large-scale training then becomes large-scale optimization. Yurii Nesterov, a famous optimization expert, gave an interesting invited talk about how to solve certain optimization problems that arise from ML in time that is logarithmic in the number of parameters.

When ML training is distributed across many computers, it is challenging to minimize the amount of communication between the computers. Training time is typically dominated by communication time.

One very nice NIPS talk described a method of performing distributed feature selection which only requires two phases of communicating models between all of the nodes. This looks promising.

Practice of Machine Learning

One quite positive trend I saw at NIPS was algorithmic and theoretical researchers examining issues that ML practitioners frequently encounter.

In the last few years, adversarial training has been a topic of research interest. In adversarial training, you don’t try to model the world as a probability distribution, but rather as an adversary who is trying to make your algorithm perform poorly. You then measure your performance relative to the best possible model that could be trained from the adversarial data, in hindsight.

A lot of the work in adversarial training has been quite interesting. At this NIPS, I saw some work that showed its practicality. It’s the nature of adversarial training to provide worst-case bounds. If you have an algorithm that is adapted to “easy data”, you normally lose the worst-case guarantees. A paper in the main conference showed that you can have your cake (perform well on easy data) and eat it too (get a worst-case guarantee). Drew Bagnell gave a clear talk at one of the Reinforcement Learning workshops that illustrated how adversarial learning is required in order to learn control policies in the real world (because you should treat your own mistaken decisions as an adversary).

There was a delightful workshop about Software Engineering for Machine Learning. Speakers from LinkedIn, Microsoft, Netflix, and Facebook talked about their experiences in putting ML into production. Some Google engineers produced a very trenchant paper about technical debt incurred by putting ML into production. I highly recommend reading it, if you are planning on doing it.

Summary

Between the progress in deep and large-scale learning, and the theoretical focus on practical issues, I learned a lot at NIPS. I’ve gone every year that the conference has existed, and I’m looking forward to the next one.

John
Learn more about my research. Follow me on twitter.

Bing brings the world’s knowledge to your Office documents

Fri, 12 Dec 2014 14:00:00 GMT

Imagine your child is writing a report about Abraham Lincoln, they just started and so far they’ve typed: “Lincoln was the 16th president of United States. He was born in…” but then realize they’ve forgotten when Honest Abe was born.

Ordinarily, they would have to leave Word, open a browser window, search for “Lincoln” – all of which takes time and breaks their work flow. Worse, their search results would have many other “Lincolns” including the car, movie and town in Nebraska. The browser search obviously doesn’t know their intent.

Well, now you have a solution.

Earlier this week, Bing and Office introduced Insights for Office, a cool new way to find the information you need right within the documents you are creating.

We encourage you to go ahead and try a free version right here – just click the previous link, choose the New blank document template, paste the above quoted text into the blank document, then select the text, right click and choose “Insights” to see this in action.

Bing indexes and stores entity data from around the web representing people, places and things. Insights for Office uses Bing’s ability to index the world’s knowledge, its machine learned relevance models along with text analytics capabilities to semantically understand the most important content in the user’s document and return the most relevant results.

Intrigued? Learn more about this cool new capability here or by clicking on the image below.

ML Blog Team

Advancing Research in Sign Language Recognition

Thu, 11 Dec 2014 14:00:00 GMT

Re-post of a recent article that ran on the

An estimated 360 million people worldwide suffer from hearing loss. But a majority of hearing individuals do not understand sign language. So communication between the hearing and the deaf can be challenging.

Now researchers are poised to make such interactions much more feasible through an easy, cost-effective and efficient prototype called the Kinect Sign Language Translator that translates sign language into spoken language – and spoken language into sign language – in real time.

Early last month, the Kinect Sign Language Working Group, a research community that includes a website for sharing data and algorithms, was established at the Institute of Computing Technology, CAS in Beijing. The community’s founding members are the CAS, Beijing Union University and Microsoft Research. This group has a very broad mission that spans machine learning, sign language, social science, and much more. We are encouraging experts from other research institutions, schools for the deaf and hard of hearing, and non-government organizations to join the Working Group.

Learn more by visiting this post or clicking on the image below.

ML Blog Team

Channel 9 Video on Azure Stream Analytics

Wed, 10 Dec 2014 14:00:00 GMT

In case you missed it: Channel 9 recently featured a discussion and demos of our preview release of Azure Stream Analytics.

“Data Exposed” is a good place on Channel 9 to learn about Microsoft’s world of data – be it relational or non-relational, established products like SQL or brand new.

The site now features some of our latest advanced analytics capabilities. In this video, Judy Meyer and Dipanjan Banik from our Azure Stream Analytics (ASA) team introduce their fully managed streaming service which can provide real-time insights into huge volumes of data in a matter of seconds.

They show core capabilities of their service including a demo of how ASA can help you perform sentiment analysis over live twitter feeds. To check it out for yourself, click here or on the image below.

ML Blog Team

Machine Learning – Hype or Reality? Microsoft ML Experts Weigh In

Tue, 09 Dec 2014 17:00:00 GMT

The recent Practice of Machine Learning Conference at Microsoft concluded with a lively panel discussion moderated by principal researcher Misha Bilenko on the topic of: "Are We at Peak ML, or at the Start of AI Takeover? Hype vs. Reality of Machine Learning.” Our panelists were:

Greg Buehrer, Partner Development Manager, Bing Ads
John Platt, Distinguished Scientist, Microsoft Research
Joseph Sirosh, Corporate Vice President of Machine Learning

This post recaps their conversation and the kinds of issues raised by our audience.

The first question played off the title of the panel: "As humans get replaced by machines, how will data scientists be useful and will they be replaced as well?"!

Greg commented that he didn't see that happening soon. Machines are good at mechanical and physical processes, but still not that great at automating decision-making especially in very complex environments. John and Joseph took the conversation further by discussing the challenges associated with employment and wealth distribution in an environment of seemingly ever-increasing automation, but the panelists admitted they couldn’t predict exactly how this trend might play out.

After discussing the Azure ML marketplace as a place where data scientists can publish their innovative ideas as web services, all panelists issued a call to action to the audience to be ever more data-driven in their work and build their ML skills, as there are many possibilities ahead of us to make products and services more intelligent.

Next, in response to an audience question, the panelists gave their opinions on whether there is a danger to using ML systems as "black boxes" inside systems such as drones, cars, etc. without fully understanding what's inside.

Joseph's opinion was that "I don't think of ML as being any different than any software algorithm collection, and there is a lot of software you can ask the same question about". He felt that there must be systems in place to ensure reliability. Greg agreed that mistakes can be made, and that experience and decision-making responsibilities have to be assigned appropriately. John then argued that "People are teaching ML incorrectly. They teach what's inside the black box, but before that you need to learn statistical hygiene: You need to have a test set, you need to not cheat, you have to do confidence intervals, and you need to worry about outliers. Learn statistical hygiene to avoid disasters". All three panelists agreed on this.

An audience member suggested that many ML use cases focus on mitigating negatives (e.g. intrusion detection, fraud detection) and asked about positive implementations. Greg suggested that avoiding these negatives in itself was a positive for customers and businesses :-) but also mentioned things such as recommendation systems which help consumers discover things that they may like. John brought up new experiences such as Office Delve which are helping people become more productive. Joseph mentioned a conversation that he had with the founder of eHarmony about how they use ML to help compatible people discover each other. He added that there are a whole host of other scenarios where ML is making some extremely positive contributions, including speech recognition, visual and gesture recognition. John closed with the tongue-in-cheek comment that if eHarmony is using ML, then "the future evolution of human DNA is being driven by machine learning."

Next, the panelists debated how privacy and ML intersect. Some of this discussion focused on the experience that Julia Angwin describes in her book Dragnet Nation, about how difficult it can be to gain true privacy in the Internet age. Joseph’s takeaway from hearing Julia speak recently was that the discussion might be changing from whether one has privacy to "whether people who have your data are using it responsibly… it's about justice, people who have your data are accountable for it, are responsible for using it in a just way."

The discussion also touched upon the enormous possibilities that the cloud opens up for data scientists and ML practitioners, as well as the potential of massive amounts of data that are becoming available. One example, for instance, focused on airplanes collecting vast amounts of weather data as they fly, which can be used to predict weather patterns much more accurately.

John noted that, if you can solve the privacy problem, then "One of the powers of data is that if you pool data together, you get more out than you put in, because you get more information by correlation". Greg added, "Not only do you want to encourage people to put data in a certain spot, but you want to ensure that the applications collecting the data collect it in the most structured way possible" so that you can make the most use of it.

Joseph chimed in with the comment that "When you put enormous compute against enormous data, and you bring machine learning to bear along with it, and the Internet of Things feeding data into the cloud…and streaming analytics running on live data…I think that in a very short time, you will see a completely different picture of analytics".

The discussion lasted an hour and the panelists addressed well over a dozen questions then, so this recap is necessarily incomplete. However, you can stay tuned to more ML happenings around Microsoft by subscribing to our ML blog feed and by following us on Twitter @MLatMSFT.

ML Blog Team

How Bing Algorithms Incorporate Content Quality to Improve the Ranking of Search Results

Tue, 09 Dec 2014 03:05:00 GMT

Re-post of an article that appeared today on

The Role of Content Quality in Bing Ranking

Millions of users come to Bing every day to find information that matters to them. In earlier generations of search, a query such as “breast cancer symptoms” would be simply treated as three keywords to match against the web. But, behind the query is a real user with a real information need and the results returned could have important ramifications. Also, much of the content matching a query such as this will turn out to be low quality articles and uninformed opinions written hastily by non-experts. User queries like this and millions of others each day motivate Bing to go beyond keyword matching and really help users by finding them content that is authoritative, useful, well written and well presented.

Learn more by reading the full article here or just click the image below.

ML Blog Team

Weekend reading - 3 recent stories

Fri, 05 Dec 2014 23:15:00 GMT

Three new stories about Microsoft ML and Advanced Analytics.

1. Fueling the Oil and Gas industry with IoT

The oil and gas industry’s supply chain starts in some of world’s most remote areas and serves consumers globally in all the places where the finished product gets consumed. The industry depends on complex and expensive equipment from hundreds of manufacturers to extract, move, refine and sell fuel 24 hours a day. One challenge and a significant opportunity for the industry is to monitor these assets and use sensor data to improve the efficiency of the system and enable innovation. Learn about how Rockwell Automation is using Azure and Machine Learing to take advantage of the Internet of Things (IoT) to bring its vision for The Connected Enterprise to life. In doing so, they are building intelligence that is transforming the petroleum supply chain, bringing enhanced productivity from the fuel source to the pump.

2. Using Visual Studio to build Apps that Integrate with HDInsight / Hadoop

Oliver Chiu talks about how Microsoft is making developers more productive with “big data” by adding additional tooling for HDInsight in Visual Studio as part of recent updates to the Azure SDK. These new VS extensions help developers to visualize their Hadoop clusters, tables and storage in familiar tools. You can now create and submit ad hoc Hive queries for HDInsight directly against a cluster from within VS, or build a Hive application that is managed like any other VS project.

3. New Azure ML book now the #1 New Release in ML, on Amazon.com

Co-authored by Microsoft insiders Valentine Fontama and Wee Hyong Tok, this new book provides an introduction to data science and ML with a focus on building and deploying predictive models. It explains the concepts of predictive analytics and ML through practical tasks and applications. Readers need to have a basic knowledge of statistics and data analysis, but not deep experience in data science. Advanced programming skills are not required either, although some R experience would prove handy.

ML Blog Team

Python Tools for Visual Studio now integrates with Azure Machine Learning

Tue, 02 Dec 2014 17:00:00 GMT

This blog post is authored by Shahrokh Mortazavi, Partner Director of Program Management on the Microsoft Azure Machine Learning team.

Two languages are closely associated with Data Science today – R and Python. In Azure ML we’ve supported R for some time – and very soon we’ll add full Python support as well. This includes a world-class Python experience in Visual Studio, in Azure ML Studio and in the browser via Jupyter/IPython. As a first step, we’re excited to announce that the Python Tools for Visual Studio (PTVS) team has added features to integrate with Azure Machine Learning APIs hosted in the cloud.

I’m also happy to announce that PTVS 2.1 RTW was recently released and is available from codeplex. Note that this is an officially supported OSS plug-in. When installed into the Professional version of Visual Studio (free, available here), you’ll have a powerful Python centric Data Science IDE that is completely free. We believe powerful open source tools such as PTVS will greatly empower developers and help democratize frontier technologies such as machine learning and advanced analytics.

PTVS 2.1: A Quick Overview

Python Tools for Visual Studio offers an IDE experience for general scripting, web programming and Data Science. With integrated IPython REPL support for smart history, shell commands and inline images, PTVS provides a great exploratory coding environment. With unique features such as mixed mode debugging of Python with C++ and remote debugging of Linux servers in Azure, Visual Studio provides a productive development environment for Python developers:

1: Multi-lingual Projects; 2: Editor with deep code intelligence
3: VS Debugger 4: Integrated IPython REPL 5: VS/Excel live bridge

For a quick walkthrough of PTVS2.1 features, take a look at this video on YouTube.

PTVS “ML Pack” and Azure ML Web Service consumption

While the focus for the 2.1 release of PTVS was Web frameworks, the team has already created a “Machine Learning Pack” which can be download from codeplex to give you a taste for ML and Azure ML web services. The ML pack has three starter templates that include everything you need from data acquisition, cleaning and training all the way to visualization using matplotlib:

Simply select your template and hit F5 to get a sense for a typical ML workflow. Then browse through the code and customize it as you like for your particular scenario. As with everything else in PTVS, the code is open source (Apache 2.0), so feel free to send us your feedback and PR’s.

Azure ML Studio is a powerful easy to use canvas that enables rapid composition of ML experiments along with 1-click operationalization. PTVS has full support for quickly building web apps and dashboards using frameworks such as Django, Flask and Bottle. The ML Pack now brings the two together via a wizard that enables easy consumption of published predictive API’s into your web app:

Simply fill out the form after you’ve published, and PTVS will generate a skeleton dashboard that you can deploy to Azure Web Sites.

IPython/Jupyter

Azure ML Studio provides a convenient drag/drop model for quickly building ML workflows and operationalizing them. PTVS provides a desktop Data Science workbench with excellent support for large projects, debugging, profiling, intellisense, git, etc. The last piece that’s missing from this picture is IPython (now the polyglot “Jupyter”), which is a browser based “notebook” REPL. Azure ML will be adding this third canvas in the near future, enabling a fully cloud hosted, cross-platform, browser based experience for data science. You’ll be able to use Jupyter on Azure ML with both Python and R. Each of these authoring environments have their own centers of gravity. Our plan is to provide an integrated experience where you can use the right tool at the right time for your project.

Conclusion

Python and its ecosystem of rich libraries is a perfect fit for Data Science. You can pair PTVS with a scientific distro such as Anaconda or Canopy today, use scikit-learn, Pandas, matplotlib, etc. for analytics / Data Science work, and deploy to a VM or Cloud Service in Azure. In the near future we plan to bring you a fully integrated Visual Studio, Jupyter and Azure ML Studio experience to maximize your productivity as developers and Data Scientists. Stay tuned!

Shahrokh

Tweet Chat with John Platt – Thu December 4th, 1pm PDT

Mon, 01 Dec 2014 14:00:00 GMT

Tweet Chat with John Platt

Thu December 4^th, 1 pm PDT

Hosted by: @MLatMSFT

Hashtag: #MLatMSFT

He is one of the leading experts in Machine Learning, a multi-patented inventor and data guru. During his Ph.D. at Caltech he even managed to discover a couple of asteroids! He is a Distinguished Scientist and Deputy Managing Director at Microsoft Research. He is John Platt.

Want to learn more about John’s story and ML at Microsoft? Do you have an ML question and want to hear the answer from an expert?

Join us @MLatMSFT on December 4^th at 1pm PDT for a one hour Tweet Chat with the man, the myth, the legend: John Platt.

ML Blog Team

Microsoft ML featured on CIO magazine, WIRED, KDnuggets and PCWorld in the past week

Thu, 27 Nov 2014 01:45:00 GMT

Microsoft’s Machine Learning technology got a bit of press coverage in the past week – here’s a quick round up of the major stories:

1. Internet of Things Helps Asthma Patients Breathe Easily
Medical device company Aerocrine is reducing device downtime and better servicing hospitals and clinics by using new IoT services from Microsoft including the Azure Stream Analytics real-time event processing engine and Azure Event Hubs scalable publish-subscribe ingestor.

2. The Internet of Anything: A Smartphone App That Lets You Control Your Office Environment
As part of a collection of workplace technologies, Carnegie Mellon University has built an app that gives workers more control over their office environments, letting them actively oversee things like lighting and temperature from their smartphones. The project, which uses Azure Machine Learning, will sell to both businesses and government agencies in the coming months.

3. Why Azure ML is the Next Big Thing for Machine Learning
“With advanced capabilities, free access, strong support for R, cloud hosting benefits, drag-and-drop development and many more features, Azure ML is ready to take the consumerization of ML to the next level.”

4. Microsoft, five other groups race toward automated image captioning
“Automated image processing could not only improve the Web's search engines, it could help you: automatically tagging all your vacation photos of the Eiffel Tower, for example, rather than hunting them down by the dates that you were actually in Paris.”

We wish our readers a very happy Thanksgiving!

ML Blog Team

AzureML Web Service Parameters

Tue, 25 Nov 2014 14:00:00 GMT

Overview

AzureML Web Service APIs are published from Experiments that are built using modules with configurable parameters. There is often a need to change the module behavior during Web Service execution. The Web Service Parameters feature enables this functionality.

A common example is setting up the Reader module to read from a different source, or the Writer module to write to a different destination. Some other examples include changing the number of bits for Feature Hashing module, or the number of desired features for filter-based feature selection module, or training and generating a forecast with newly incoming data in a time series forecasting scenario, among other things. The parameters can be marked as required or optional at the time of creation.

How to set and use Web Service Parameters

In the following example we’ll walk through setting up and using the feature in AzureML Studio. (Click here to get started with AzureML)

We will first create a predictive Web Service from one of the sample experiments. We will parameterize the API to enable the client calling it to write the results of the prediction to an Azure Storage Blob location different from the one specified in the Experiment. This gives the client control over where to write the results of the prediction.

1. Build a Training experiment and save the Trained Model

a. Start with the Sample 5: Train, Test, Evaluate for Binary Classification: Adult Dataset

b. Click Save As, then name the experiment as Web Service Parameters Example - Training

c. Remove some nodes to simplify the graph (see screenshot below), then Save and Run

d. After run is done, save the Trained Model by right-clicking on the lower pin of the Train module, then selecting Save As Trained Model. Call it Trained Model Web Service Parameters.

e. Note the newly saved Trained Model in the left menu under Trained Models.

2. Build a Scoring Experiment

Now that we have a Trained Model, we will build a Scoring experiment which we will publish as a Web Service. To do that:

a. Click on Save As and create a copy of the Experiment; call it Web Service Parameters Example – Scoring.

b. From under Trained Models in the left menu, drag the Trained Model Web Service Parameters and add it to the graph.

c. Remove the Two-Class Boosted Decision Tree learning algorithm, the Train and Split modules (we have already trained a model and saved it in the above step so we don’t need to train again).

d. Click on Project Columns and add Income to the list of excluded columns (this is the target value we will predict). The graph should now look like below.

3. Set a Web Service Parameter

Here is where we will use the Web Service Parameters feature to dynamically change the Writer module’s destination at run time.

a. Drag a Writer module under the Score module, then connect Score to Writer.

b. Click on the Writer to select it. Then view its properties on the right hand side of the screen.

c. Enter the account information for AzureBlobStorage option of the data destination. This information is available through the Azure Management Portal’s Storage option. (You would need to set up your Azure Storage in advance for this).

d. Note the icons next to the module properties. Click on the icon next to the Path to blob, and select Set as web service parameter.

e. Set the path to container1/output1.csv

f. Note the Web Service Parameters list item added to the Properties with the Path to blob beginning with… under it.

g. To rename the parameter, click on the name and type in blobpath, then hit enter. Note the property’s new name (bottom of the list).

h. Click run.

4. Publish the Web Service

a. Set the Input Port of the Web Service by right-clicking on the input pin (top) of the Project Columns and selecting the Set as Publish Input. Then right-click the output pin of the Score Model and select Set as Publish Output.

b. Click run, then click on Publish Web Service after run is completed successfully.

c. In the resulting Web Service Dashboard, note the API Key. We will copy this into the C# code later.

d. Click on the API help page link for the Batch Execution option (second entry). Note the Sample Request Payload shows the newly added parameter – blobpath.

e. Click on the Sample Code link on the Web Service help page to view the C# sample code. We will paste this code into a C# Console client App.

5. Build a client application to call the new Web Service

a. Start Visual Studio, and create a new C# Console Application. (File->New->Project->Windows Desktop->Console Application). Call it AzureMLClientApp.

b. Return to AzureML Studio API help page, and copy the code from the C# sample into the Program.cs file of AzureMLClientApp. (Note and follow the instructions in the sample code about installing libraries and setting references).

c. Update the parameters defined as constants in the code. A few to note:

i. BaseUrl: the Post URL on the Web Service’s API help page for the Batch Execution Service (see Step 4.e above)

ii. StorageAccountKey: the key from Azure Management Portal -> Storage -> Manage Access Keys

iii. StorageContainerName: name of the Storage Container from Azure Management Portal -> Storage -> Containers

iv. InputFileLocation: file location for the input file that we will do prediction on e.g. C:\Temp\censusinput.csv. To download a sample input file used for testing the API, return to the Training or Scoring Experiment created above, and right click on the output pin of the Adult Census Income Binary Classification Dataset (the top first module used in either Experiment), then select Download.

v. OutputFileLocation: file location for local output file generated after prediction e.g. c:\Temp\censusoutput.csv.

vi. apiKey: to get this, in AzureML Studio, click on Web Services in the left menu bar, then click on the Web Service name (Web Parameters Example – Scoring). Then copy the API Key from the Web Service Dashboard.

d. Set the Web Service Parameter’s value

In Program.cs file’s InvokeBatchExecutionService method, we set the value of “blobpath” to the desired blob name (e.g. container1/outputFromWebParam.csv). This is will be used as the value of the parameter we had set when setting up the Experiment.

e. Optional: Tweak the final Console.Writeline statement to show the blob path we are passing in

f. Run the C# application

g. Validate the result

The output file containing the prediction results is written to the Storage blob path specified in the client application (in Azure Management Portal -> Storage -> Containers):

Wrapping Up

We walked through setting up the Writer module in an AzureML BES service with a parameter to specify the destination Storage blob path at run time. During that example, we:

Created a Training Experiment and saved a Trained model
Created a Scoring Experiment using the Trained model
Used a Writer module and the new Web Service Parameters feature to set the Storage blob path as an input parameter
Published a Web Service from the Experiment, and used the Batch Execution Service (BES) to do batch prediction on an input file
Used the Web Service Parameter to set the location of the output of the prediction at run time
Wrote to the destination Storage blob path specified by the client application

We will be releasing a new feature in the near future, called Retraining APIs, which will allow programmatic retraining of trained models using this feature by setting the location of input file at run time. We will have more details on that later.

ML Blog Team

Rapid Progress in Automatic Image Captioning

Wed, 19 Nov 2014 00:10:00 GMT

This blog post is authored by John Platt, Deputy Managing Director and Distinguished Scientist at Microsoft Research.

I have been excited for many years now in the grand challenge of image understanding. There are as many definitions of image understanding as there are computer vision researchers, but if we can create a system that can automatically generate descriptive captions of an image as well as a human, then I think we’ve achieved the goal.

This summer, about 12 interns and researchers at Microsoft Research decided to “go for it” and create an automatic image captioning software system. Given all of the advances in deep learning for object classification and detection, we thought it was time to build a credible system. Here’s an example output from our system: which caption do you think was generated by a person and which by the system?

An ornate kitchen designed with rustic wooden parts
A kitchen with wooden cabinets and a sink

[The answer is below]

The project itself was amazingly fun to work on; for many of us, it was the most fun we've had at work in years. The team was multi-disciplinary, involving researchers with expertise in computer vision, natural language, speech, machine translation, and machine learning.

Not only was the project great to work on: I’m also proud of the results, which are in a preprint. You can think about a captioning system as a machine translation system, from pixels to (e.g.) English. Machine translation experts use the BLEU metric to compare the output of a system to a human translation. BLEU breaks the captions into chunks of length (1 to 4 words), and then measures the amount of overlap between the system and human translations. It also penalizes short system captions.

To understand the highest possible BLEU score we could attain, we tested one human-written caption (as a hypothetical “system”) vs. four others. I’m happy to report that, in terms of BLEU score, we actually beat humans! Our system achieved 21.05% BLEU score, while the human “system” scored 19.32%.

Now, you should take this superhuman BLEU score with a gigantic boulder of salt. BLEU has many limitations that are well-known in the machine translation community. We also tried testing with the METEOR metric, and got somewhat below human performance (20.71% vs 24.07%).

The real gold standard is to conduct a blind test and ask people which caption is better (sort of like what I asked you above). We used Amazon’s Mechanical Turk to ask people to compare pairs of captions: is one better, the other one, or are they about the same? For 23.3% of test images, people thought that the system caption was the same or better than a human caption.

The team is pretty psyched about the result. It’s quite a tough problem to even approach human levels of image understanding. Here’s a tricky example:

System says: “A cat sitting on top of a bed”

Human says: “A person sitting on bed behind an open laptop computer and a cat sitting beside and looking at the laptop screen area”

As you can see, the system is perfectly correct, but the human uses his or her experience in the world to create a much more detailed caption.

[The answer to the puzzle, above: the system said “A kitchen with wooden cabinets and a sink”]

How it works

At a high level, the system has three components, as shown below:

First, the system breaks the image into a number of regions that are likely to be objects (based on edges). Then a deep neural networks is applied to each region to generate a high-level feature vector that captures relevant visual information. Next, we take that feature vector as input to a neural network that is trained to produce words that appear in the relevant captions. During that training, we don’t hand-assign each word to each region; instead, we use a trick (called “Multiple Instance Learning”) to let the neural network figure out which region best matches each word.

The result is a bag of words that are detected within the image, in no particular order. It’s interesting to look at which regions caused which words to be detected:

Next, we put together the words in a sensible sentence, using a language model. You may have heard of language models: they take a training corpus of text (say, Shakespeare), and generate new text that “sounds like” that corpus (e.g., new pseudo-Shakespeare). What we do is train a caption language model to produce new captions. We add a “steering wheel” to the language model, by creating a “blackboard” of the words detected from the image. The language model is encouraged to produce those words, and as it does, it erases each one from the “blackboard”. This discourages the system from repeating the same words over and over again (which I call the Malkovich problem).

The word detector and the language model are both local, meaning they only look at one segment of the image to generate each word, and only consider one word at a time to generate. There is no sense of global semantics or appropriateness of the caption to the image. To solve this, we create a similarity model, using deep learning to learn which captions are most appropriate for which images. We re-rank using this similarity model (and features of the overall sentence) and produce the final answer.

This, of course, is a high-level description of the system. You can find out more in the preprint.

Plenty of research activity

Sometimes, an idea is “in the air” and gets invented by multiple groups at the same time. That certainly seems to be true of image captioning. Before 2014, there were previous attempts at automatic image captioning systems that did not exploit deep learning. Some examples are Midge and BabyTalk. We certainly benefited from the experience of these previous systems.

This year, there has been a delightful Cambrian explosion of image captioning systems based on deep learning. It appears as if many groups were aiming towards submitting papers to the CVPR 2015 conference (with a due date of Friday, Nov 14). The papers I know about (from Andrej Karpathy and my co-authors from Berkeley) are:

Baidu/UCLA: http://arxiv.org/pdf/1410.1090v1.pdf

Berkeley: http://arxiv.org/abs/1411.4389

Google: http://googleresearch.blogspot.com/2014/11/a-picture-is-worth-thousand-coherent.html

Stanford: http://cs.stanford.edu/people/karpathy/deepimagesent/

University of Toronto: http://arxiv.org/pdf/1411.2539v1.pdf

This type of collective progress is just awesome to see. Image captioning is a fascinating and important problem, and I would like to better understand the strengths and weaknesses of these approaches. (I note that several people used recurrent neural networks and/or LSTM models). As a field, if we can agree on standardized test sets (such as COCO), and standard metrics, we'll continue to move closer to that goal creating a system that can automatically generate descriptive captions of an image as well as a human. The results from our work this summer and from others suggests we're moving in the right direction.

John
Learn more about my research. Follow me on twitter.

From Data to Operationalized ML in 60 Minutes!

Tue, 18 Nov 2014 17:00:00 GMT

This blog post was co-authored by Debi Mishra, Jacob Spoelstra and Dmitry Pechyony of the Information Management & Machine Learning team at Microsoft.

Microsoft has a strong track record for crafting tools such as our Office apps or Visual Studio which millions of users find relatively easy to use. These apps have set the industry standard for individual and team productivity in terms of how quickly a new user can learn the tool, use it to accomplish their tasks and automate tedious or mundane activities, so they can better focus on their job.

Great tools spark creativity and do not get in the way of the user. They make seemingly difficult things easy to accomplish. Great tool often eventually end up creating an entirely new breed of empowered users. Take for instance how, in the 1990s, Microsoft Visual Basic expanded the base of software programmers by millions worldwide – users who might otherwise not have taken up such a pursuit.

When we created Azure Machine Learning Studio, our target audience included all data scientists – from aspiring students and hobbyists to enthusiasts and seasoned experts. Our primary vision for the tool is the title of this blog post – we truly believe that the proof of the ease and power of a tool such as Azure ML Studio can be measured by the time it takes for a typical data scientist, even someone relatively new to the field, to go all the way from raw data to a fully operationalized web service, powered by the intelligence harnessed from that data.

In this post, we talk about how Azure ML Studio is helping drive greater productivity and ease of use for our Data Scientist audience. In particular, we focus on how the tool enables users to stand up intelligent web services powered by predictive analytics in a matter of an hour or less.

The ease of use starts with Azure ML being cloud hosted. There is no software to install, no hardware to manage, no dependency on IT and practically no constraints on disk space or CPU cycles. With our free option – which no longer requires an Azure subscription or credit card – you can start developing ML models in a matter of minutes and you can do so from anywhere, using any device and using nothing but a web browser. You can start work on an ML experiment at your workplace, pick things up from where you left them during your commute and – later the same evening – continue running your experiment from your tablet at home.

Model Authoring Experience

Azure ML Studio lets you set up experiments as simple visual data flow graphs, with an easy to use drag, drop and connect paradigm. The tool also makes many common data science tasks easy and intuitive. For instance, you can do the following:

Bootstrap from a set of pre-authored templates of fully working experiments, representing common data science patterns.
Compose an experiment workflow using “modules” as algorithmic building blocks. All our modules are plug ‘n play with strong “typing” and have reasonable default settings pre-selected. So simply dropping in a module without any customization works as a reasonable starting point.
Bring the data from multiple sources including SQL, Hadoop, OData, and Azure Storage.
Use our powerful built-in suite of world class ML algorithms. All our learners can be used in the same way and swapped with each other as needed, so there is little effort to use a new learner.
Handle feature selection with feature selection and parameter sweeper modules.
Easily compare the performance of several algorithms and choose the one that works best for your problem. Since our data flow graphs support multiple parallel paths, you can make side-by-side comparisons easily.
Use our built-in support for R. Over 400 of the most popular CRAN packages come preinstalled. This allows your existing R skills and scripts to be directly brought into and integrated seamlessly into Azure ML – see an earlier post on this topic. Our team is working to add Python support soon.
Easily revisit prior runs of an experiment, using our lineage tracking capability – so you can get a complete view of your prior experimentation.
Avoid programming for a large number of common tasks, which lets you focus on experiment design and iteration.
Collaborate with others worldwide on your project. Azure ML Studio lets teammates virtually look over each other’s shoulders, share data and intermediate results, and pick up on your work where others left off.

This screenshot of a typical Azure ML Studio experiment showcases many of these points:

We are gratified to receive many positive comments from our customers regarding our ease of use. Here is one such comment, from Yogesh Dandawate of Icertis Applied Cloud: “The standout benefit for us was to be able to quickly build and test predictive models and verify their results. There is no cognitive overhead to learn a new scripting or coding language”.

Models in Production

Data Scientists want to see their models deployed and functional in the real world. A common frustration is how hard it is to put built models into production, and indeed, a large percentage of models never see real world usage. Azure ML Studio makes it super simple to deploy a model into production use, with a single click. The operationalized workflow – containing the data transformations and model – are deployed as web services supported by the fully managed, secure, reliable, and elastic Azure cloud infrastructure, which provides worldwide access. The model that you build can be called from any modern programming language used by the engineering team that consumes the model. As you publish the model, Azure ML Studio provides you with sample code in C#, R or Python for immediate consumption of the published web services within the app or a productivity tool like Excel. Azure ML also provides an operationalization layer for R code. You can easily transform your existing R code into a cloud-based model with REST APIs. This is a critically important feature given how large the R developer community is, and given the fact that they have historically not had such as easy way to operationalize R code. The blog post Running R in the Azure ML cloud on R-bloggers discusses how Azure ML enables easy deployment of R models.

Our team has much work ahead as we aim to make our tool even more widely accessible and productive for our users. For instance:

We have heard from you that full REPL capability inside the Studio is desirable.
We are adding support to allow the dataset schema information to “flow” down the workflow, so that the column selector works even before you have run the experiment.
We are working on “composite modules” that will enable users to save common workflows as pre-fabricated compositions which they can reuse across many experiments.
Our design team is conducting user studies to create a continuous feedback loop and we are combining those inputs with our service analytics, to ensure that our team is fully aware of areas where the tool could do even better.

“The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds—even non-data scientists.” says Bertrand Lasternas of Carnegie Mellon University. Hans Kristiansen of Capgemini agrees: "Azure ML offers a data science experience that is directly accessible to business analysts and domain experts, reducing complexity and broadening participation through better tooling."

If you have not done so already, please go to www.azure.com/ml and start using Azure ML Studio for free. Be sure to check out our samples, create a new experiment and stand up your own ML web service – not in weeks and months as it used to take – but all in matter of an hour or less! Send us your feedback and thoughts.

We believe that, with the right future investments, Azure ML can truly help attract many more practitioners to the data science community, just as Visual Basic earlier did for an earlier generation of software developers.

Debi, Jacob and Dmitry
Follow Debi on Twitter

Free webinar: Operationalizing R as a Web Service

Fri, 14 Nov 2014 02:20:00 GMT

R is the most widely used language today for machine learning, but its power is sometimes limited by gaps in the technology meant to bring it to life. In this webinar, learn how you can use your existing skills in R in new ways, including deploying models as web services with a few clicks. The first half will be presentation content and the second will be open Q&A for everyone interested in optimizing their machine learning solutions.

Friday, November 14th, 2014

9:30 A.M. – 10:30 A.M. PDT

Register now!

Forget the pollsters: Microsoft's Bing predicted midterm election with 95% accuracy

Thu, 13 Nov 2014 23:30:00 GMT

This is a re-post of an article from NetworkWorld.

The search engine continues its track record of astonishingly accurate predictions.

"Now that the dust has settled from the elections, Bing Predict has won out again with a 95% accuracy rate in calling the House, Senate, and Governor's races. It got 34 out of 35 Senate races correct, 419 out of 435 House seats correct, and 33 out of 36 Governor's races correct. That's a better prediction rate than even Nate Silver’s lauded FiveThirtyEight blog."

How We Share Machine Learning Knowledge at Microsoft

Wed, 12 Nov 2014 17:00:00 GMT

We recently concluded the Fall 2014 edition of our Practice of Machine Learning Conference (PMLC). Over 1,700 Microsoft employees attended the two day event, which featured 60 talks on a broad spectrum of areas ranging from new algorithms to ML applications such as anomaly detection and fraud. Tutorials covered such topics as feature engineering, labeling, Azure Machine Learning, multi-world testing and more. A Poster & Demo Reception saw more than 50 presenters. And our first-ever Azure ML Cloud App Contest featured many interesting contenders.

To share a flavor of the event with you, we boiled our 2 day event into a 2 minute video:

The best part was that ML practitioners from dozens of teams and locations around the world got a chance to connect in person, discuss the latest advances in ML and learn from one another. Presenters from Beijing, Cambridge, Herzelia, Hyderabad and 15 other cities flew in to participate in person and share their work. Some of the most popular sessions included:

Automatic Image Captioning at a Human Level of Performance
Beating Paul the Octopus: How Bing Uses Web and Social Data for Predictive Modeling
Customer Churn Prediction Service on Azure ML
Exploring ML with scikit-learn
Introduction to Fireworks Algorithm Optimization
Real-Time Stream Analytics Using Azure Stream Analytics

At his keynote talk, Joseph Sirosh, Corporate Vice President of Information Management & Machine Learning (IMML), talked about the newly emerging Data Science Economy and the opportunities afforded by Azure ML for data scientists, including the ability to self-publish and monetize their skills through the Azure Marketplace in a manner similar to how developers monetize their skills through app stores today. This echoed his message at the recent Strata + Hadoop 2014 event which had a similar theme.

In the first-ever Azure ML Cloud App Contest, participants built Azure ML apps of their choice that addressed real-world scenarios. The winning app, Dr. Pig, created by a Microsoft team in China, helps small-scale pig breeders forecast future prices and profits and mitigate risk by helping with decisions such as the type of pig to breed.

The conference concluded with a closing panel designed to stimulate spirited discussion – in “Are We at Peak ML? Hype vs. Reality of Machine Learning”, John Platt, Distinguished Scientist at Microsoft Research, and Greg Buehrer, Partner Development Manager, joined Joseph to debate the future directions of ML.

The PMLC is held twice a year. Between conferences, the Microsoft ML community gathers regularly for smaller in-person and online events where practitioners share their experience and expertise. Members also participate in ML-focused hackathons and internal forums, and have access to resources such as the online Machine Learning University, which pulls together course materials on the most-requested ML topics. These activities are designed to allow ML practitioners from around the world share their knowledge, help one another and make Microsoft products and services better through the power of ML.

To learn more about ML at Microsoft, you can subscribe to our ML blog feed or follow us on Twitter @MLatMSFT. And, for those of you practitioners who did not know yet, we recently made Azure ML available free of charge without a subscription or credit card – just click the blue “Get Started Now” button at the Azure ML Preview page and get going today.

ML Blog Team

Microsoft Research Grants Available for Azure, Including Machine Learning

Tue, 11 Nov 2014 19:05:00 GMT

This article is a re-post from the Microsoft Research Connections Blog.

A year ago, the Microsoft Azure for Research project began as a small effort to help external researchers and scientists (and Microsoft) understand how the cloud could accelerate research insights. The project enables researchers to take advantage of cloud computing for collaboration, computation, and data-intensive processing, and gives researchers access to events, online training, technical papers and more.

The project also features a very popular award program, which provides qualified research proposals with substantial grants of Azure storage and compute for one year. We got over 700 proposals in the past year and from all seven continents, including one from researchers in Antarctica! We granted awards to over half the submitted project proposals, facilitating research in a wide range of disciplines, including computer science, biology, environmental science, genomics, and planetary science.

The program also issues special-opportunity RFPs including one for Azure Machine Learning. Click here to learn more about our research collaboration, including how to apply for a grant.

ML Blog Team

VentureBeat: Microsoft gives out free access to its Azure Machine Learning service

Fri, 07 Nov 2014 23:55:00 GMT

Re-post of an article from VentureBeat earlier this week.

"... you’ll welcome a recent development coming out of the PASS Summit in Seattle. Microsoft executives on the scene there announced that the company is opening up Azure Machine Learning to free access. That means you don’t need to plug in your credit card information if you want to try it."

Information Week: Dell Bolsters Analytics Software, Taps Microsoft Azure ML

Fri, 07 Nov 2014 23:50:00 GMT

Re-post of an article that ran earlier this week, from Information Week.

Dell extends its big data analysis capabilities, adding natural-language processing and integrating Microsoft Azure Machine Learning services.