Whether you are an algorithm developer in a growing startup company, a data scientist in a university research lab, or a kaggle hobbyist, there may come a point in time when the training resources that you have onsite no longer meet your training demands. In this post we target development teams that are (finally) ready to move their machine learning (ML) workloads to the cloud. We will discuss some of the important decisions that need to made during this big transition. Naturally, any attempt to encompass all of the steps of such an endeavor is doomed to fail. Machine learning projects come in many shapes and forms and as their complexity increases so does the undertaking of making such a significant change as migrating to the cloud. In this post we will highlight what we believe to be some of the most important considerations that are common to most typical deep learning projects.

Whether you are an algorithm developer in a growing startup company, a data scientist in a university research lab, or a kaggle hobbyist, there may come a point in time when the training resources that you have onsite no longer meet your training demands. In this post we target development teams that are (finally) ready to move their machine learning (ML) workloads to the cloud. We will discuss some of the important decisions that need to made during this big transition. Naturally, any attempt to encompass all of the steps of such an endeavor is doomed to fail. Machine learning projects come in many shapes and forms and as their complexity increases so does the undertaking of making such a significant change as migrating to the cloud. In this post we will highlight what we believe to be some of the most important considerations that are common to most typical deep learning projects.

Machine Learning is considered a part of Artificial Intelligence in the field of Computer Science. It often uses statistical techniques to give computers the ability to "learn" ( progressively enhance the performance of a particular task) with data, without being specifically programmed. Machine Learning is often related to computational statistics, which also concentrates on prediction -making through the use of computers. Machine Learning has wide applications as it is used in various industries like Banking, Retail, Publishing, Financial Sector etc. Top companies like Facebook and Google to push pertinent advertisements which are based on users past search behaviour. Machine Learning is basically used for managing multi-dimensional and multi-variety data in dynamic environments.

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NIIT, a global leader in skills and talent development, today launched a course in Web App Development with MEAN Stack under the DigiNxt Series. The company has recently ventured into Digital Transformation to offer pioneering programs to young aspirants wishing to enter the digital services industry, as well as to IT professionals wishing to reskill themselves for the new digital world. The cutting-edge program will use the student-centred pedagogy of project-based learning to help them carve a successful career in the emerging digital era. Some of the famous web applications like LinkedIn, Netflix, Uber, Paypal, etc. have been built using MEAN Stack. AngularJS, Node.js (MEAN) represents a group of open source technologies which are known to synergize well together, thereby empowering students to launch their own web and mobile apps.