Big Data, Cloud & DevOps

There’s a lot of talk about the applicability of artificial intelligence (AI) and deep learning to taming the vast quantities of data that modern Operations teams and their tools deal with. Analyst reports frequently tout AI capabilities, no matter how minor, as a strength of a product, and the lack of them as a weakness.

The booming growth of machine learning and artificial intelligence (AI), like most transformational technologies, is both exciting and scary. It’s exciting to consider all the ways our lives may improve, from managing our calendars to making medical diagnoses, but it’s scary to consider the social and personal implications.

The emergence of “digital twin” technology will revolutionize how industrial enterprises approach manufacturing operations. Digital twins unite physical entities with virtually-modeled “twins” based on technologies like AI and Big Data derived from IoT sensors, ultimately improving the design and execution of manufacturing and maintenance life cycles as well as creating new revenue streams and services. Vince is a platform and product executive spanning cloud, mobile, big data, analytics, and artificial intelligence offerings. He is a leader of global product management, design, and GTM teams that consistently delivered outstanding business results.

In today’s big data world, AI and machine learning applications already analyze massive amounts of structured and unstructured data and produce insights in a fraction of the time and at a fraction of the cost of consultants in the financial markets. Moreover, machine learning algorithms are capable of building computer models that make sense of complex phenomena by detecting patterns and inferring rules from data — a process that is very difficult for even the largest and smartest consulting teams. Perhaps sooner than we think, CEOs could be asking, “Alexa, what is my product line profitability?” or “Which customers should I target, and how?” rather than calling on elite consultants.

In financial services, the dangers associated with monetizing big data are nearly as great as the rewards. The promises of machine learning, data science and Hadoop are tempered by the realities of regulatory penalties, operational efficiency and profit margins that must quickly justify any such expenditure.

AI will have a complex relationship with humans that will change over time: While certain jobs will become automated, AI is more often poised to augment human labor and decision-making. Longer-term, many applications will be designed to empower humans with non-human capabilities, memory, experiences, and knowledge. Many ethical, philosophical, cultural, societal, and business norms will be forced into re-assessment.

AI will have a complex relationship with humans that will change over time: While certain jobs will become automated, AI is more often poised to augment human labor and decision-making. Longer-term, many applications will be designed to empower humans with non-human capabilities, memory, experiences, and knowledge. Many ethical, philosophical, cultural, societal, and business norms will be forced into re-assessment.

Ready to start your Big Data Training? Browse courses developed by industry thought leaders and Experfy in Harvard Innovation Lab. We, at Experfy, recently came across an article from Ramesh Dontha on Big Data terminology. In this article Ramesh managed to give a clear and easy to understand description of… well let’s just say not the easiest

Work is not what it used to be. The very concept of work has evolved considerably over the centuries, as newer technologies have become integrated into how we function individually as well as a society. In engineering, work is defined as the product of force and distance. Early machines amplified force and turbocharged human productivity,

This is Part One in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part One is: “What is Data Science?”.