Online Learning is a branch of Machine Learning that has obtained a significant interest in recent years thanks to its peculiarities that perfectly fit numerous kinds of tasks in today’s world. Let’s dive deeper into this topic.

What exactly is online learning? In traditional machine learning, often called batch learning, the training data is first gathered in its entirety and then a chosen machine learning model is trained on said data: the resulting model is then deployed to make predictions on new unseen data. This methodology, despite being wildly popular, presents several kinds of drawbacks and limitations when applied to…


Human and machine communication towards conversational analytics

In recent years, smart speakers like Amazon Alexa, Google Home, and Apple HomePod have increased their market share, and according to the forecast, their popularity will continue to grow. Lots of people have one or more of these speakers at home, and they are used for different things, from the easiest one like setting an alarm to the toughest one like playing games only using the voice. These speakers are also used in professional and commercial environments. …


Customer experience is the key factor for a business to emerge from increasing competition. Though 84% of executives agree on the importance of customer experience and prioritize customer-centric strategies, only 14% of them report that they have strong capabilities in that area. Actually, today’s current business context has imposed on companies a series of strategies and adaptations that have become mandatory in order to survive the continuous market stress: the customers change their opinion, they’re really attentive, knowledgeable, and conscious so companies have to be able to understand their needs in a timely manner. …


Retailers need to embrace an aura of newness

Retailers recognize that COVID-19 will have a significant impact on their business and it’s time to think through the longer-term implications. According to analysts’ studies, there are key areas where retail execs should be focusing their attention in today’s highly-fluid social, economic environment. They need to restructure their operating models as well as rethink and evolve the clients’ relationship. Retailers have to find a new way to maintain trust in brand, products, and services, reset expectations for today, and recover the customer experience.

For sure e-commerce, chatbots, and mobile apps are offering some small rays of hope in the shutdown…


Predictive maintenance for industry 4.0 helps determine the condition of in-service equipment in order to estimate when maintenance should be performed. Its main goal is to prevent asset failure by analyzing production data to spot patterns and predict issues before they happen. Until now, factory managers and machine operators carried out preventive maintenance, scheduling it at regular intervals, but this is an ineffective activity that consumes unnecessary resources and drives productivity losses.

Thus, it’s not a surprise that predictive maintenance is quickly emerging as a number one Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies…


How streaming data and real-time analytics can reduce operational costs

Vehicles sharing operators have gained some powerful momentum in the past three years, and they contribute to sharing mobility growth, reaching a market value of over 100 billion USD. However, they’re entering a critical new phase of development, where the goal is the delivery of real improvements against key metrics and priority outcomes. This development demands data to be embedded in service design to improve decision-making, support real-time operational control, increase service quality and efficiency, and improve engagement with customers, businesses, and other stakeholders.

Therefore, there is immense potential for the better use of data in this sector, even though…


Because of a number of reasons, both technical and people-related, it is hard to accomplish Big Data projects. Here the main ones

Poor Integration

Poor integration is one of the major technical and technological problems behind the failure. Actually, integrating siloed data from heterogeneous sources to get the outcomes that organizations want, linking multiple data and building connections to siloed legacy systems is easier said than done.

Technology Gap

Companies often try to merge old data silos with new sources, without success. This is because with different architectures data processing needs to be done newly: use the current tools for an on-premises data warehouse and integrate it with a big data project, which will become too expensive to process new data. …


By Saverio Veltri

The history of Database management systems could be interpreted as a Darwinian evolution process. The dominance of relational databases gives way to the data warehouses one, which better adapt to the earliest business intelligence requirements; then, alongside the rise of the most popular big data platforms such as Hadoop or spark, comes the era of the NoSQL databases, which were designed to privilege scalability among other features (instead of consistency for example).

However, what has happened during the years, is more likely a specialization process.

In fact, we are experiencing the coexistence of many DBMS paradigms, such…


By Gloria Ronzoni— Data Scientist

Telemetry Sports

The history of F1 motor racing and the use of telemetry as a way to monitor car setup and performance dates back to the 80s. The first electronic systems were installed onboard the car, collected information for only one lap and the data were then downloaded when the car was back in the garage. The explosion of computing capabilities, in the 90s, contributed to the growth of intelligent data usage in the F1 and the need to monitor data with a much higher frequency from a car. Nowadays each car has from 150 to 300…


by Roberto Bentivoglio

Big Data is probably one of the most misused words of the last decade. It was widely promoted, discussed and spread around by business managers, technical experts, and experienced academics. Slogans like “Data is the new oil” and all its different shades were widely accepted as unquestionable truth.

These beliefs pushed really forward the Hadoop technologies. Its stack, formerly developed by “Yahoo!” and lately owned by the Apache Software Foundation, was recognized like “The” Big Data solution.

Many companies started to offer commercial, enterprise-grade and supported versions of Hadoop until it’s started to be experimented and adopted…

Radicalbit

We provide Event Stream Processing products designed to manage the entire data lifecycle over streaming oriented platforms, with Machine Learning integration.

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