Machine learning is an interesting trend to observe today. Companies are placing big bets on machine learning algorithms and thinking how to apply it in a different business scenarios.
InfoWorld article How IBM, Google, Microsoft, and Amazon do machine learning in the cloud provides an interesting insight on how Google, Microsoft and IBM are implementing machine learning as a service. Read the article – it give you few interesting data points.
My special attention caught Google Prediction API. Here is the passage that can give you a general idea of the solution.
Google Prediction API is a proprietary API maintained exclusively by Google. The latter, despite the unassuming name, is a broadly inclusive service that allows users to upload data and train models in the manner of of Microsoft Azure Machine Learning Studio. (Data can be derived from Google services like Google BigQuery.)
Amazon Machine Learning is similar to Google Prediction API in that models can be trained against data and used to make predictions. It’s a deliberately simplified service, either for the sake of appealing to developers who only want to solve a specific, narrow problem or because Amazon wanted to test the market waters first.
In both Amazon and Google’s cases, their targets are developers both with narrowly defined needs and with data already on those clouds — the “just enough” model.
It made me think about applying some of available machine learning services to existing data. Cloud technologies are allowing easy integration of services. The ability to use one of available machine learning cloud services can be an interesting opportunity to explore.
Out of 3 services, I picked up Google Predictive Analytics API as an example. The following video can give you a general idea how the service can be used to generate prediction about real estate property cost based on some basic information
The example made me think how similar approach can be used to predict some critical information about cost, potential chance of failure or other characteristics based on a set of product data. Think about scenario of change and decision that can be taken based on some analysis made by one of the available machine learning cloud services.
What is my conclusion? With the latest development of cloud PLM, more data about product is available via cloud services, which makes data integration much easier. Machine learning cloud services is an interesting opportunity that can lower barrier for cloud PLM systems to use machine learning in some practical examples – Engineering change order, product cost analysis and others. Just my thoughts…
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