ML deployment & monitoring made simple

Two critical aspects of the Machine Learning lifecycle are operationalization and monitoring. Radicalbit platform allows you easily put your models in production in a fraction of the time.

What is event stream processing?

Event Stream Process  works with an infinite stream of data with continuous computation, as it flows, with no need to collect or store the data to act on it. It’s  used to query continuous data stream and detect conditions, quickly, within a small time period from the time of receiving the data .

    Why ESP is necessary?

    Some insights are more valuable shortly after it has happened with the value diminishes very fast with time. Stream Processing enables such scenarios, providing insights faster, often within milliseconds to seconds from the trigger.

      “Event stream processing is the ability to work with an infinite stream of data with continuous computation, as it flows,
      with no need to collect or store the data to act on it.”

      Kafka Streams in Action – William P. Bejeck Jr.

      Why using Event Stream Processing?

      Stream Processors

      Streaming systems have finally reached a point of maturity

      Batch using streaming

      It’s possible to obtain batch features directly using modern stream processors

      Stream vs Batch

      Streaming is a superset of batch

      Bounded vs unbounded data

      Bounded data: finite stream of data
      Unbounded data: infinite stream of data

      Near Real-time vs Real-time

      There is a delay introduced between the occurrence of an event and the processing of the data

      The events are processed as soon as possible by the stream processor

      Data analytics and machine learning

      Machine learning advancements such as neural networks and deep learning algorithms can discover hidden patterns in unstructured data sets and uncover new information, vital for companies. Actually, when machine learning and streaming data meet, amazing things can happen. Radicalbit Data Management Platform offers a set of tools specifically designed around data scientist’s needs, a simple yet powerful way to reduce the time to market in deploying machine learning enhanced analytics applications.

        Radicalbit Technology Stack

        From Lambda to Kappa architecture

        The solutions developed by Radicalbit are based on the Kappa Architecture, a powerful and agile approach able to manage natively streaming data sources together with batch.


        Popular Big Data Architecture for data processing. It is typically batch based, and treat streaming sources in a separate flow that needs to be merged at a later stage.


        Streaming-Oriented, Event based architecture for Data Processing. It Matches Streaming AND Batch requirements in a unique flow.

        Core Technologies

        At Radicalbit we like to explore: here’s some of the core technologies we’re using within our platform -either as enabling frameworks or as programming techniques.


        Stream processing framework for distributed, high-performing, always-available, and accurate data streaming applications


        Event broker / Distributed Streaming Platform


        Distributed Cluster computing framework

        AKKA / PLAY

        Reactive Application Programming Frameworks


        Enterprise Application Container Platform


        Container orchestration system


        Machine Learning and A.I. platform


        XML-Based predictive model interchange format


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