FLYR Labs is focused on the relentless application of advanced and intuitive technologies that help transportation leaders unlock their ultimate potential. Starting with airlines, we provide the Cirrus Revenue Operating System that reshapes how travel and transportations businesses plan their commercial operation. We displace legacy data, forecasting, pricing, and reporting solutions with a single enterprise SaaS platform that leverages the latest advancements in deep learning, cloud computing, and user experience. Our hyper-accurate contextual forecasts enable the most effective scheduling, marketing, and leadership decisions while directly managing the pricing for billions of dollars worth of product and revenue. We are looking for an experienced Senior Data Analyst to join our ML Analytics team.

From food to cars to complex manufacturing machinery, quality is a top concern of manufacturers. Factors such as safety, efficiency, and reliability affect product quality and ultimately influence customer satisfaction. Sourcing, design, testing, and inspection all play a crucial role in ensuring products meet the bar when it comes to quality. Product inspections at early stages in the production cycle help reduce risks and cost. While inspections can be conducted at any point throughout the production process, the goal is to identify, contain and resolve issues as quickly as possible.

One of the definitional debates that bedevils the artificial intelligence (AI) field is whether machine learning is an AI-based method or technology. Or is it just an analytics-based activity? After all, it is statistical in nature, and attempts--as virtually analytical methods do--to fit a line or curve to a set of data points. And what difference does it make? Basic machine learning is practically indistinguishable from predictive analytics.

We recently announced how Perspica's IT incident management system is using AI to leapfrog traditional monitoring tools using advanced analytics. Let's take a closer look at why monitoring tools aren't enough for today's complex applications. Knowing whether a resource is "up or down" doesn't help you fix the problem. Monitoring tools were originally created to help us understand when there was a problem with any of our infrastructure elements. Letting us know whether a server needed to be rebooted or that you'd run out of memory was helpful then, but in today's complex applications knowing that your application has had alert because of a service level violation still leaves a long way to identify and understand how to fix the issue at hand.

The application of machine learning to predictive analytics continues apace as a way to improve IT operations, data security and business intelligence. Among those offering frequent platform upgrades is real-time "operational intelligence" specialist Splunk Inc., which this week rolled out the latest versions of its IT, security and analytics packages that seek to "operationalize" machine data. San Francisco-based Splunk (NASDAQ: SPLK) said machine learning is integrated as a core capability in its latest package of IT, security and analytics offerings in the form of packaged or custom algorithms intended to leverage growing volumes of machine data. Use cases for its enterprise, IT services, security and user behavior analytics products include: "focused investigation" of IT and security incidents to detect data patterns and anomalies; reducing "alert fatigue" by identifying normal patterns for specific use cases; proactive maintenance; demand forecasting, managing inventory; and adjusting to changing business conditions by analyzing historical data. "The enterprise machine data fabric is the foundation for managing and deriving insights from that data at scale," Splunk President and CEO Doug Merritt asserted in a statement.

SIOS Technology Corp., the industry's leading provider of software products that help IT ensure the performance, efficiency, and high availability protection of business critical applications, today announced it will co-host a live one-hour webinar featuring ActualTech Media Partner and VMware vExpert Scott D. Lowe who will show how using new machine learning based analytics tools can resolve application performance issues in virtualized environments.

Machine Learning (ML) is becoming a universal concept across industries. However, the manufacturing industry trails behind other industries in widespread implementation of machine learning techniques, as the industry remains heavily dependent on individual experience-based decision making. Integrating Big Data analytics with Machine Learning can deliver insights that immensely benefit the manufacturers. It can help them improve process efficiencies and cut operational costs, and ultimately enhance customer experience. Consider the scenario of a stamping plant in the plant shop floor of an automotive OEM.