Machine Learning model serving tools comparison - KServe, Seldon Core, BentoML
Intro Machine Learning is now used by thousands of businesses. Its ubiquity has helped to drive innovations that are increasingly difficult to predict…
Read moreThe rapid growth of electronic customer contact channels has led to an explosion of data, both financial and behavioral, generated in real-time. This data presents a valuable resource for marketing, sales, business process optimization, and fraud prevention strategies.
As the volume of real-time data grew rapidly, Millennium Bank required a solution that could efficiently analyze large data streams in real time. The objective was to harness this data to drive informed decision-making and enable instant actions across customer communication channels and banking applications.
Bank Millennium, in collaboration with GetInData, implemented a cutting-edge system based on open-source components such as Apache Flink and Apache Kafka. This solution enabled efficient processing of streaming data, data enrichment, and instant actions, such as customer communication through electronic channels and real-time operations via banking apps. A key advantage of the system is its user-friendly interface, which allows business users to configure and implement changes quickly and independently—a crucial feature in the fast-evolving business landscape.
A significant advantage is also the possibility of self-configuration by the business user, ensuring quick introduction of changes and new functionalities. This is crucial in a changing business environment.
GetInData's involvement included developing and scaling the solution architecture, its implementation, deployment, and integration with data sources. Example business scenarios were also created as a basis for further expansion carried out independently by the Bank Millennium.
The implemented solution has found application in the area of marketing campaigns, allowing for communication with the customer and offering them closely tailored products at the right time, as well as in the area of fraud prevention, significantly reducing the time needed to adapt anti-fraud scenarios to changing requirements - which is crucial in this area.
Intro Machine Learning is now used by thousands of businesses. Its ubiquity has helped to drive innovations that are increasingly difficult to predict…
Read morePlanning any journey requires some prerequisites. Before you decide on a route and start packing your clothes, you need to know where you are and what…
Read moreCustom components As we probably know, the biggest strength of Apache Nifi is the large amount of ready-to-use components. There are, of course…
Read moreApache NiFi, big data processing engine with graphical WebUI, was created to give non-programmers the ability to swiftly and codelessly create data…
Read moreNowadays, data is seen as a crucial resource used to make business more efficient and competitive. It is impossible to imagine a modern company…
Read moreMy goal is to create a comprehensive review of available options when dealing with Complex Event Processing using Apache Flink. We will be building a…
Read moreTogether, we will select the best Big Data solutions for your organization and build a project that will have a real impact on your organization.
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