Prevent System outage with Machine Learning

Failures in the functioning of equipment are inevitable in any kind of industry. The repair and recovery time often leads to big financial losses each year. But we have machine learning and predictive analytics as a solution. The machine learning models are trained to learn the ideal functioning of the machinery. Then this functioning is compared with how the machines are working at present. So if even a minor change occurs somewhere, it doesn't go unnoticed. Then, with the help of predictive analytics the loss that can take place in the near future is predicted. This adds to the huge advantage of the firms. Learn more about this in the article written by Mike Reed   (manager of analytical services for Avantis PRiSM software) at: http://www.intelligentutility.com/article/16/02/saving-money-and-man-hours-machine-learning

Machine Learning gets better with "human in the loop"

Machine Learning is getting easier and accessible because of the computing power becoming affordable. Moreover, big enterprises are making their algorithm open source. This is because data is the food. More data an algorithm gets, the better it becomes. But from step 1, making algorithms, feeding data in humans play a significant role. Sometimes there are outliers which the algorithms cannot interpret. Here human intervention is necessary. They manually check such pieces. But when these are fed into algorithms, they make them robust by identifying outliers. Thus, human intervention is both necessary for accuracy and training. Read more at: http://insidebigdata.com/2016/01/11/human-in-the-loop-is-the-future-of-machine-learning/

Dr. Robot: Future of Health Care with AI

We can’t imagine today’s world without IT sector. In each and every sector such as finance, medical, engineering, politics and many more IT is playing an important role by improving accuracy and perfection. Similarly in the field of health science, Artificial intelligence and robotics is playing very important role in transforming the health care industry as well as also helping with taking better decisions and improving public health. According to an estimation more than 6 billion dollars: That’s how much health care providers and consumers will be spending every year on artificial intelligence tools by 2021. If we believe on people related to health care industry then it is clear that in future AU will be everywhere- from diagnosing cancer to providing weight-loss coaching. No one can doubt on the ability of AI to sort through scads of information, and remember everything it has ever seen, could enable a digital version of Dr. House, the brilliant diagnostician from the eponymous TV show. "At first, it's a complete mystery, it could be one of ten different things," he says, about the process in the show, and real life, called differential diagnosis. AI can take care of more factors so it may help doctors to take better decision in care of “democratize” diagnosis. “Democratize” diagnosis is general cardiologist rather than a team with different sub-specialties. To know more read the article by Sean Captain at: http://www.fastcompany.com/3055256/elasticity/paging-dr-robot-the-coming-ai-health-care-boom

Is Big Data Changing Disruptive Innovation

Despite the many differences in application, most people agree on that the disruptive innovation are: 1. Cheaper 

2. More accessible 

3. And use a business model with structural cost advantages. 

Due to the presence of these characteristics in disruption, it's difficult for an existing business to respond to competition. To read more above disruptive innovation follow: https://hbr.org/2016/01/how-big-data-is-changing-disruptive-innovation

What 2016 holds for Machine Learning?

The evolution of Machine Learning (ML) is affected by the approach of the tech giants towards it. Open Source Platforms and the data sources also have an important impact on the ML models. Tech giants have realized the importance of ML, and this is becoming the new normal for them. They are now focusing on providing ML models as a Service. These are built for the common usage, not just for the data scientists. Most of the softwares being used for ML are open sources, thus affecting the market of other softwares making sources. Tools like Apache Spark are going to dominate the market. Read more about it on: http://www.infoworld.com/article/3017251/data-science/what-machine-learning-will-gain-in-2016.html

Difference between Hadoop and Apache Spark

Hadoop and Apache Spark are seen as the competitors in the world of big data, but now the growing consensus is that they are better convention in together. Here is a brief look at what they do and how they are compared.  1. They do different things: Both are the big-data frameworks, but they do not serve the same purposes. Hadoop is a distributed data infrastructure. It also Indexes and keep track of that data, enabling big-data processing and analytics. On the other hand, Spark is a data processing tool. Secondly, both can be used individually, without the other. 3. Spark is faster 4. You may not need Spark's speed: Spark is fit for real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring. 5. Failure recovery: differently, but still good. Read more at: http://www.computerworld.com/article/3014516/big-data/5-things-to-know-about-hadoop-v-apache-spark.html

Cloud Computing is taking up a large part of IT budgets for businesses

from the various surveys by various companies it is clear that cloud computing is taking large percentage of IT budgets for businesses, and the use of cloud computing is becoming pervasive across all aspects of businesses. According to one report it is found that following usages of cloud computing by businesses: # 81% - sales and marketing. #79% - business analytics. #79% - customer service. #73%- HR and Payroll. To read more about this follow the article by Dick Wiesinger (author) at:  http://formtek.com/blog/cloud-computing-businesses-increasingly-are-shifting-to-public-and-hybrid-cloud-usage/