Big Data Analytics as a Service: The next big thing IT organizations have generated, collected, and stored vast amounts of data. And now, IT is being asked to provide the infrastructure to perform analytics on this data. But this task is a resource-intensive proposition and is being solved by cloud technology in the form of Big Data-as-a-Service.

The cloud is a fantastic resource for Big Data because of its scalability, and also because it is extremely economical. As datasets grow larger, some organizations are moving their datasets…

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4 steps in building effective machine learning models Building machine learning models that have the ability to generalize well on future data requires thoughtful consideration of the data at hand and of assumptions about various available training algorithms. Ultimate evaluation of a machine learning model’s quality requires an appropriate selection and interpretation of assessment criteria.

Machine learning consists of algorithms that can automate analytical model building. Using algorithms that iteratively learn from data, machine learning models facilitate computers to find hidden insights from Big Data…

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3 reasons why blockchain will bring value in Procure-To-Pay process Procure-to-Pay consists of the process of obtaining and managing the raw materials needed for manufacturing a product or providing a service. It includes the transactional flow of data that is sent to the concerned supplier as well as data that surrounds fulfillment of the actual order. Blockchain in Procure-to-Pay processes can bring greater value.

Procure-to-Pay (PTP) is the multi-step process that connects a client with one or more service/product providers. It also allows for the identification and authentication of stakeholders, service provisioning budgeting, invoicing,…

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3 best practices in building deep learning models Deep learning, also referred to as hierarchical learning, is a branch of machine learning. Building Deep Learning models involve designing a particular set of algorithms, which attempt to model high-level abstractions in data.

In the financial services industry, usage of Deep Learning models is becoming popular as they facilitate more accurate predictive analytics, which has helped in improving forecasting, recommendations, and risk analysis….

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Taking insurance to next level with machine learning Machine learning refers to a set of algorithms that use real-time or historical data for predicting current or future outcomes. Use of machine learning in insurance use cases helps in achieving three main objectives: improving compliance, cost structures and competitiveness.

What does machine learning in insurance industry do? Machine learning algorithms and technologies help insurance industries in reviewing, analyzing and assessing information in pictures, videos and voice conversations.  These systems…

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