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 some…

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. …

A wealth of interesting technologies and methodologies has risen in recent years under the “Cloud Native” umbrella name, and their impact in our lives as developers has been very deep.

We were once used to have big monolithic applications, hosted on enterprise application servers deployed on virtualized (and frequently expensive) hardware; now we have containers, cheap cloud computing/storage/everything, better implementations of agile methodologies, powerful architectural patterns like microservices, wonderful schedulers like Kubernetes, we have those “unicorn-like” creatures we call DevOps Engineers (or should we call them SREs? that’s another story) and so on….

All these technologies/methodologies allow us to build…

by Marinella — UX Manager in Radicalbit

Implementing BEM in a cluster of products made with React.js following Marie Kondo’s secret of happiness

Any frontend developer will have experienced the pleasure of opening the newly released page with the Chrome inspector finding a clear and semantic clean code. Not even Marie Kondo could do better!
How many times has it happened to you to review some old code and think: “gosh, how bad is this? ? who assigned the classes? What a mess!“.
At first glance, it might be the work of the former nice but bungling colleague, but Git doesn’t lie and Visual Studio quickly reveals the author — what the heck — it was me to…

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…


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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