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Azure Machine Learning

What is it?

Azure Machine Learning is a cloud analytical service which allows to use machine learning algorithms. Azure ML makes it possible to quickly and easily create predictive models, recognize speech and images and process texts. It offers a variety of machine learning algorithms suited to a number of business needs. Because Azure ML is a part of Microsoft cloud, it is not necessary to purchase costly equipment in order to launch a solution using this technology. The entire solution is well scalable.

Not only does Azure ML provide a predictive model building tool, but it also offers a service which may be used to deploy solutions. What is more, it makes it possible to easily launch ready-for-use web services which may then be used by desktop- or mobile-type applications.

Predictive analytics is based on algebraic and statistical theories which, drawing on historical data, allow predicting future trends and events. Azure Machine Learning is particularly efficient in this regard, because it makes use of a ready algorithm library (Cortana Intelligence Gallery) or modifies and combines them appropriately with the help of a user-friendly web interface. In case of Azure ML the business value presents itself in no time.

Azure Machine Learning has everything needed to build a cloud predictive solution, from algorithm library through environment to model building and easy model deployment in the form of a ready-for-use web service. Azure ML is built so as to not impose any architecture. It operates irrespective of existing IT infrastructure and it is technologically agnostic – it integrates with libraries written in Python or R.

What is it used for?

Example uses of Azure Machine Learning:

Fraud detection

detecting suspicious financial transactions, spam filtering

Sound-based speech recognition (Speech API)


recommending content suited to particular users, predictive content loading, improving UX applications

Predicting market needs

predicting needs e.g. for goods available in a particular region, for processing power in a region, for energy resources

Targeted marketing

allocating appropriate advertisements to users, choosing appropriate marketing campaigns based on user profiles, cross-selling, up-selling

Content classification

categorizing documents, allocating documents to individuals (e.g. connecting candidate CVs to HR departments)

Resignation forecasting

finding subscribers who may unsubscribe, finding clients who may switch from a free plan to a paid subscription

Customer service

predictive forwarding of messages from clients (e.g. directly to an appropriate department based on message content), social media analysis.

It can also be used for less commercial purposes, e.g.:

Image-based face recognition (Face API)

Decoding brain signals, analyzing neuropsychological data

Car telemetry

Plane and train delay

Estimating the risk of disease incidence (e.g. cancer, heart disease)

Image- and handwriting-based character recognition

Our experience

BlueSoft successfully uses the Azure Machine Learning technology at its clients representing a variety of industries (e.g. financial, telecoms or life science), while our expertise allows us to fully utilize its possibilities.

Our company has ample experience in the realm of business analysis, which helps our clients choose appropriate issues that can be improved using machine learning algorithms and then deploy them with the help of our team of experienced developers, analysts and architects. Azure Machine Learning is a platform which, if properly used, is highly beneficial to organizations, however it requires knowledge of Data Science in order to extrapolate maximum business value from the data at hand. However, it goes without saying that with the help of an experienced team and a platform like Azure Machine Learning it is possible to fully utilize the potential of machine learning while at the same time exercising cost control.

BlueSoft has successfully implemented many projects in this area. We will happily present our portfolio directly as well as answer more questions about technology itself and benefits to be brought by its implementation.

See other technologies, which we use in this area

Machine Learning