Machine Learning is a phrase that you would have already heard it lot of times, but what does it actually mean? Machine Learning can be understood as the foundational discipline which is the basis around the modern statistical data analysis.
Data Science is emerging on a daily basis and to meet the challenges of Big Data, there is a definite need for us to understand and learn practicing the same becomes very crucial.
Now the advent of the same has been made into the Azure’s space as well, which is what we would be taking a peek at. Let us not make any further delay and let’s dig into the concepts of Azure’s Machine Learning.
Just in case if you do not know or you have not familiarized yourself with the Azure family of products – Azure is a Microsoft offering in the Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) lanes.
Azure Machine Learning has been added a new addition to the family of Azure Cloud products in the month of February 2015. The moment this has been launched into the markets, it has proven its mettle as a game changer in the cloud realm (for the problem of big data processing).
After overlooking the great success of the Hadoop based Azure HDInsight and also Power BI for Office 365, Microsoft Azure has taken the next big step towards owning the market share in the Big Data space with its grand release of Azure Machine Learning (also coined as Azure ML in short).
It was one of the first attempts made to take the Predictive analytics space to public cloud offerings made it look like it is the next logical step towards bigger scale consumerization of Machine Learning. Azure Machine Learning has done this in style, also focusing the ease of usage for the developers.
Azure Machine Learning runs on an Azure public cloud offering, that in turn lets the users not to worry about purchasing any specific hardware or software to get their hands going on Machine Learning.
This also handles both the deployment and maintenance of this without any problem at all. People with no absolute background in Machine Learning will also be able to get themselves working with the advent of Azure’s ML Studio as the integrated development environment.
This IDE provides data models via drag-drop gestures and also allows to build models with simpler data flow diagrams.
ML Studio for the developers is a very good choice to start their Machine Learning journey without any hassles, as it minimizes most of their coding and in turn saving them a lot of time through the libraries that are provided via ML Studio.
This being said, seasoned professionals and data scientists can take advantage of the tool and get their hands dirty with the strong support with Azure ML support to R programming language for Big Data analytics and Machine Learning.
If you have any existing R code, you can simply drop it into Azure ML Studio and get started, if you are to develop new code then you can do that with more that with 350 plus R packages that are supported within Azure ML Studio.
Azure Machine Learning is an offering built upon many successful Machine Learning capabilities of Microsoft products, resources and services. Azure’s Machine Learning shares similar features as like the real-time predictive analytics that the new Personal Assistant in Windows Phone (Cortana) uses.
Bing has outshined by predicting the results of more than 95% of the US mid-term elections, thus making it an ideal choice straight away on a big-scale. This will definitely put a thought in your thinking cap to check out the most powerful cloud based predictive analytics.
Comparision With IBM Watson
Azure Machine Learning was in comparison with IBM Watson for quite a long time. But Microsoft has ensured that they surpass this comparison by announcing Azure Marketplace’s Machine Learning capabilities including Bing Speech Recognition control, Microsoft Translator, Bing Synonyms API, Bing Search API and etc. As on today, Azure Marketplace hosts more than 25 Machine Learning APIs in itself.
It has now become the most convenient platform for the data scientists to build custom built web services and public APIs. Azure is in turn earning from the charges that they charge for its usage.
Microsoft Azure have left no stone unturned in putting their efforts to reach the maximum share of the Machine Learning audience to market themselves as the best.
They’ve tried a lot of things to make themselves the best for the people to use their services. As a result of which, now you can access Azure Machine Learning layer without providing any of your credit card details (which was requested earlier for security purposes).
This has encouraged professionals from many lines of businesses and streams to get their hands on with the product – like the DBA’s, Developers, BI professionals and also the amateur Data Scientists who wanted to prove their skill with the use of ML Studio (of course).
With the kind of response that they have received on this product launch, it seems to be like they’ve hit the right path to their long term success in the Machine Learning realm.
In this article, we have tried to introduce you to the concept of Machine Learning in specific and then have introduced the same concept put to use in Azure’s context.
We have tried to understand the nitty gritty details of what Azure Machine Learning can achieve and what is possible with it, if you were to get to business with it.
Hope you are able to understand the concept and the necessary details but should you need any further read, please go through the official documentation of Azure Machine Learning and get yourself acquainted with the same (using the one month subscription that can be availed for free).
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