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AI make it possible for machine to think like human and take decisions based on learning from past experiences. AI is concerned with building smart machines that requires human intelligence to perform task.
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions.
Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.
You’re faced with intense disruption as the digital-first economy is growing new roots very rapidly and the habits of customers is changing for the foreseeable future. We take you where you need to be with a focus on the right outcome. Our expertise is the design and development of truly customer centric.
Google Cloud Machine Learning Engine allows startups to build machine learning models that work on any data, of any size. Trained models are immediately ready for use with Google’s global prediction platform and fully integrate with Google’s infrastructure, APIs, and data services.
The ELK stack is a log management platform comprised of three open source projects: Elasticsearch, Logstash, and Kibana. It is designed to provide users with the features of these three solutions within a single image. It combines deep search and data analytics and centralized logging and parsing displayed in a powerful data visualizations.
Integration Services provides a control flow for performing work that is tangentially related to the actual processing that happens in data flow. Integration Services provides a full-featured control flow to support such activities.
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How Machine Learning Model Works
The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily.
To test whether this algorithm works correctly, new input data is fed into the Machine Learning algorithm. The prediction and results are then checked.