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Machine Learning Applications: Machine Learning in the Enterprise

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The good news for enterprises is that all the data they have been saving for years can now be turned into a competitive advantage and lead to the accomplishment of strategic goals. Revenue and senior management teams are concentrating on how they can capitalize on machine learnings' core strengths to transform the strategic vision of their businesses into a reality. These teams are focusing on business outcomes first and are looking for machine learning to accelerate and simplify, determining which factors most influence buying behavior and lead to goals being accomplished. My colleague, Elliot Yama, recently wrote about why it is necessary to leverage machine learning to drive business outcomes. Predicting propensity to buy across channels, making personalized recommendations to customers, forecasting long-term customer loyalty, and anticipating potential revenue and credit risks of buyers are some specific applications of machine learning right now.


What Is ML.NET

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Are you a .NET developer? Want to bring machine learning into your applications? ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET. ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more. ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.


Are you monitoring your machine learning systems?

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When it comes to monitoring machine learning systems, how to monitor them currently consists of gluing together many pieces of tech. Is there a better way to hunt down and eradicate the bottlenecks in your ML systems? Even the simplest machine learning systems consist of many moving parts. The most basic I've built was deployed to a single server and the most complex consisted of more than 40 micro services feeding into a large processing and analysis cluster (and don't get me started on all the ways we stored the data). In all cases we used monitoring.


Quantum Stack One

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The Quantum Stack One kit is a Raspberry Pi based 4 node cluster computer based on the Raspbian platform. This kit is complete and comes pre configured to run MPI (Message Passing Interface) software, and Python based neural networks.. This kit is perfect for MPI developers as it allows them to develop there applications at home, without requiring specialized access to a MPI cluster. You can build applications based off of pythons neural network libraries, including TensorFlow, Scikit learn, and Pandas. Use the Quantum Stack One to research and develop applications for finance, machine learning, artificial intelligence and more.


Machine Learning Applications by Industry

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Machine learning's ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes. Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.