Encouraging Solutions in Technology.by Emma Thompson Writer
You must have heard the term machine learning, many times. It is a subsidiary term for artificial intelligence or AI. Both originated from MIT, USA in 1950s. It is a result of an advanced technology that keeps self driving cars from crashing. Machine learning systems have challenged human intelligence. The demand for these systems has arisen sharply since a couple of years have been witnessing successive breakthroughs in technology, cost friendly engines evolved for computing and doles of data for the learning systems to frisk upon and sieve through.As the volume and variety of data expands and multiplies, data scientists spend most of their time in consolidating, re-arranging and managing the problem of amassed data. One such solution is in store with a general Spark machine learning library is designed for its simple, scalable and easy access to and integration with other tools. With Stream analytix data can be scaled, compiled, consolidated and sieved through faster. Problems or hitches emerging or arising from them can be detected and corrected swiftly, speedily and easily. For its expanse, coverage, reach and easy adaptability to diverse andcomplex configurations; and simple answers to crack tough logistics, the Spark machine learning is an increasingly sought after and sophisticated algorithm tool kit, to put it simply.
Traditional uses of tools like Python and R, popular languages with data scientists, are often limiting because of slow data movement, analytical sampling (often inaccurate) and transferring development knowledge or learning to an environment of production often proves miscalculated and with glitches; given to extensive re-engineering.
A unified and powerful learning engine, Spark machine learning helps solve complex learning problems such as graph computation, streaming and real time interactive query processing. Spark machine learning provides all this and more at a greater scale and interactivity. Its users are data scientists and data engineers.
Spark machine learning algorithms are not only used in most cases it is also provides for users community to build upon for specialized use cases. Spark machine learning has the single or common framework for solving several varying problems; instead of learning and maintaining different tools for each scenario.
The common business use cases requiring intervention from Spark machine learning library, are:
Marketing and Advertising: It helps to know the products to recommend to each user to optimize engagement and revenue. Knowing theuser site behaviour increases the probability of clicking on available or possible ads.
Security monitoring/ fraud detection/risk assessment/ network monitoring: detecting anomalous and malicious user behavior.
Many compelling and competitionridden business modules and scenarios are being computed by Spark machine learning technology. Its features are driven and sparked by simplicity, scalability, streamlining and compatibility.
Streaming data analytics is a process of analysis thatperforms action on real time data. It is applied to external data sources, whichit processes and selects from and sets into motion a desired data flow.Streaming data analytics, also termed event stream processing, is an analysis of a large volume ofin-motion data called event streams. These streams comprise of an event of actions or triggers like an equipment breakdown, big financial transactions and so on. In streaming data analytics the data is derived from devices, sensors, web sites, social media feeds, applications, infrastructure systems and others.Thus, Spark machine learning and streaming data analytics provide encouraging insights into data for businesses.
Created on Oct 7th 2017 01:17. Viewed 631 times.
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