In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.
To shed light on how IT operations teams are dealing with working in challenging environments, Pepperdata has carried out a period of customer research. This report revealed a wealth of insights regarding the condition of enterprise workloads that lack the benefits of observability and continuous tuning. Combined with cloud computing statistics and a more general understanding of big data industry trends, there is much to learn here about the present and future of the data analytics industry.
In this special guest feature, Simran Bagga, Program Director – AI and Machine Learning at Concord Technologies, take a holistic look at ways to complete a successful AI project through building the right team. The roles on the AI team should be determined by the company’s business processes around defining and creating solutions to problems.
In this contributed article, Fluency CEO and Founder Chris Jordan discusses the inevitable extinction of Moore’s law. 90% of the world’s data has been produced over the last two years, yet companies only analyze 12% of it. With Big Data only continuing to grow, how can more innovative data storage solutions, such as the cloud, effectively respond to this level of growth?
In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.
In this installment of “AI Under the Hood” I introduce “Flippy” by Miso Robotics. Flippy works in fast-food kitchens, operating a frying station for example. The product was decades in the making in terms of research in robotics and machine learning. Flippy is an amalgamation of motors, sensor, chips and processing power that wasn’t possible until just the past few years. The robotic arm is poised to become a regular fixture in high-volume kitchens nationwide in the coming year.
In this special guest feature, Michael Kanazawa, EY Global Innovation Realized Leader and EY Americas Advisory Growth Strategy Leader, discusses why data scientists and engineers should be working with designers, futurists, and business executives as the way to lead the future. Failing to do so risks being consigned to the past.
New research published by SnapLogic, provider of the Intelligent Integration Platform, reveals that 83% of organizations are not fully satisfied with the performance and output of their data management and data warehousing initiatives. IT leaders cite a growing number of disconnected applications and data sources, outdated legacy systems, and slow and manual data movement as reasons for their frustration, all of which are stalling progress and costing them millions.
In this contributed article, Magnolia Potter believes that the use of big data in medical research and advancement is of paramount importance. Artificial intelligence and machine learning are pioneering the ethical collection of medical data, the discovery of new drug therapies, and improved outcomes for patients. By analyzing public health concerns in real-time, big data can advance medical research in multiple fields, improve patient care, and prevent the spread of deadly diseases.
In this contributed article, editorial consultant Jelani Harper discusses how many organizations have struggled to harness the IoT’s scale, size, and speedy datasets for a cogent business use case justifying investing in this expression of big data. If the principal challenge is making sense of this continually generated streaming data in real-time, the solution is unequivocally as straightforward as it is effective: Master Data Management.