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This blog post is authored by Joseph Sirosh, Corporate Vice President of Machine Learning at Microsoft.
Last week, Microsoft announced a preview release of Azure Machine Learning (Azure ML) which is now available for customers and partners to try. Azure ML is a fully managed service in the cloud that allows you to publish advanced analytic web services in minutes and build robust enterprise grade applications. Because ML is a new science to many customers and partners, I’m also excited to introduce, just in time for this year’s Worldwide Partner Conference, our new online Machine Learning University (MLU). MLU is a collection of online learning assets to help partners get up and running on Azure ML. It includes walkthroughs of the data science lifecycle from importing and cleaning data to building predictive models and deploying them as production web services. MLU gives partners access to in-person training events, regular product updates and other valuable Azure ML resources.
My earlier post talked about why Azure ML changes the game for building ML applications. This post describes how our partners are using it to rapidly build novel solutions for our customers.
Azure ML partners, with their wealth of specialized knowledge in analytics and vertical expertise are helping customers transform their mountains of data into actionable insights. Partners such as MAX451, Neal Analytics, OSIsoft, and Versium are already deploying enterprise grade predictive analytics solutions for our customers with Azure ML. The breadth of solutions they are building is quite remarkable. Let me share four stories and quotes.
Operating over 1,000 stores, Pier 1 Imports aims to be their customers’ neighborhood store for furniture and home décor. They recently launched a multi-year, omni-channel strategy called “1 Pier 1” with a key goal being to understand their customers better and serve them with a more personalized experience across all interactions and touch points with the Pier 1 brand.
MAX451 has built an Azure ML solution to predict what a customer’s future product preferences might be and how they would like to purchase and receive these products. To quote Eric Hunter, Executive VP of Marketing at Pier 1 Imports:
Deepening our customer relationship is important to us. Gaining better insight into our data enables us to be there for her when, how and where she wants to shop, and with predictive analytics, we can invite her back to shop by featuring a product we know she’ll love. Whatever the medium may be, a more personalized message will likely encourage her to visit Pier 1 Imports again sooner… During this test phase, we have been able to improve the accuracy of predicting which product might speak to her next by more than 40 percent. Historically, translating data into great, usable information has been rather slow. Now we can reduce that time to a matter of days.
Andrew Laudato, CIO at Pier 1, had this to say:
Pier 1 Imports is helping prove Microsoft can take something as complex as advanced predictive data analytics and machine learning and make it accessible via the cloud. We are especially pleased that our analysts can focus on the results and not worry about the complex algorithms that are used to generate this data. We are extremely pleased with how quickly the team was able to get to meaningful results during this project. We enjoyed working with MAX451 – Pier 1 Imports is pleased with the results of working with a small and nimble partner.
And MAX451 CEO, Kristian Kimbro Rickard, had this to add:
At MAX451, we operate our entire business in the cloud, and our services and products are geared towards customers who are either already in or are migrating to the cloud. Microsoft’s machine learning products do not require an army of data scientist consultants to help customers. We are small, agile, and we move quickly, and we wanted to keep it that way – Microsoft’s machine learning products allow us to continue providing the same great services we always have, without straining our recruiters to find elusive, highly-skilled, highly-paid consultants.
Neal Analytics has built an Azure ML solution that is helping a large ecommerce site optimize their marketing spend on search terms, to drive traffic to their site. Search companies use auctions to rank ads on different search terms, balancing bids with content quality. The solution developed by Neal Analytics allows this customer to predict how much of a bid increase they need to spend in an auction in order to move up to the position they want for a given search term. Since competitors don’t tend to change their bids on search terms regularly, having this sort of timely, in-the-moment response to bid strategy is giving this customer a competitive advantage.
Neal Analytics had to build a predictive modeling solution for optimizing the bids for a large set of low frequency key words. The solution had to be easy to deploy and maintain. They wanted to avoid the need to stand up a net-new R/Linux computing stack to handle the volume. They wanted to enable rapid turns in deploying, testing, and refining their model to stay current with trends. Azure ML was a good fit to allow their data scientists to focus on their job and not be distracted by the complexity of setting up a big data computing infrastructure. Neal Analytics CEO and President Dylan Dias had this to say:
…because Azure ML is built on Azure, we enjoy the scalability that is seamlessly built in. Speed. Accuracy. TCO – Azure ML trumps other options out there. The learning curve with Azure ML is the shortest. It’s also much easier to drive adoption because of the short time-to-operationalize cycle. I am able to scale my precious data science talent. Relatively inexperienced analysts are now effective in their jobs. Azure ML helps our data science practice to improve time-to-insight and time-to-action metrics significantly (2-4 times quicker). We can do more with less, which results in happier clients.
The Center for Building Performance and Diagnostics at Carnegie Mellon University develops integrated hardware and software solutions to improve the efficiency of buildings on the CMU campus while achieving higher occupant comfort. Over the past decade, the center has conducted thousands of field surveys and measurements with a view to identify critical factors that affect occupant satisfaction. External factors such as weather forecasts too help predict cooling or heating energy consumption, of course. Using all this data, the center wanted to create a system to increase the overall energy performance of their buildings. ML was viewed as a critical component of any solution.
The center worked with OSIsoft, first to collect the real time data mentioned above using the OSIsoft PI System and then to develop a system to predict energy consumption across buildings, detect faults, take actions to mitigate issues in real-time and deliver cost savings. CMU has seen up to 30 percent energy reduction in some buildings after this system was deployed. Bertrand Lasternas, a CMU Researcher working on this project, had this to say:
A web-based, platform independent, machine learning solution was extremely appealing to us… The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds, even non-data scientists. The Azure ML solution provides comparable accuracy with a more user-friendly set up and better integration with existing systems. A RESTful API is key to a seamless and successful integration... Data handling is the biggest advantage as a seamless stream can be set up very quickly and be integrated into existing solutions.
Gregg Le Blanc is Director of Research and Innovation at OSIsoft and responsible for evaluating new technologies. He has evaluated several ML technologies and here’s what he had to share:
We found Azure ML has the right balance of readiness and capabilities. While our infrastructure has long enabled real-time operation intelligence, the holy-grail is to predict issues before they are seen in real-time sensor-data. Using Azure ML we are investigating the ability to predict the implications of different actions based on acquired data in the PI System and store the data within the PI System for exploratory investigations. The result will be the ability for our customers to predict more and test less, enabling our customers to find the optimal balance for delivering operational excellence in the shortest possible time.
Versium operates a predictive analytics scoring service called LifeData™. Pulling together over 400 billion real-life attributes across disparate sets of data such as purchase interests, social behavior, demographic data and financial information, Versium creates unique insights into customer behavior and helps companies leverage these insights in their promotion and marketing campaigns.
Versium is working with a major retail customer to help them detect fraudulent purchases of gift cards. This retailer already has an existing rules-based system to detect such fraud, but it generated many false positives (i.e. erroneous prediction of fraud). Minimizing such errors while stopping fraud was an important success criteria for this customer. Versium was able to quickly put together a predictive modeling solution on Azure ML, which, in a test run, showed that only 6 percent of 1000 transactions that had been denied by the old rules-based system were actually fraudulent – numbers that translate into much higher customer satisfaction, higher revenue and considerable value for this retailer. Here’s what Chris Matty, CEO of Versium, had to say:
Main advantages [of Azure ML] are in being able to interactively visualize the whole machine learning process, data and metrics of the model, being able to publish a web service quickly after the model is built. From my perspective, the solutions we deploy are very high value and mission critical – e.g. fraud prevention. So accuracy, speed and security are critical and I see all of these as value points in the technology. We deploy many scores and being able to build, tune and validate a model within days is a strong value benefit. Leveraging the Azure ML platform enables us to create and deploy a predictive score that uses Versium's proprietary LifeData™, in combination with our partners' enterprise CRM, marketing, or other internal data elements in a matter of days.
Often, the successful application of machine learning requires not only great tools but deep expertise in a domain, painstaking work to acquire and understand the client’s data, and experience in integrating with the client’s software solutions. Our partners bring the much needed expertise spanning a variety of industries. They share our passion for helping customers transform data into actionable business insights and are blazing the trail on cloud hosted ML solutions. Boundless opportunities await.
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