Can domain experts be so happy with a tool that the experience can be likened to nirvana? This provocative question is intended to start a discussion about the features needed to make real-time data products accessible to ordinary (non-technical) people. Zbigniew Małachowski presents his own list of required features. But are they enough to trigger nirvana-like relief?
Webinar video: Real-time data processing for the people
Abstract:
Acting on data in real time, such as fraud detection, next best action, streaming ML, clickstream analysis, IoT sensor analysis, etc., requires algorithms that are rarely trivial enough to be created with no code: just drag and drop.
In the streaming world, the reason why domain experts find themselves forced to go into deep technical details of Kafka, Flink, Spark, REST, etc is that while many platforms are quite successful in abstracting Kafka, Flink, Spark, etc, they are just simple visual overlays on the (streaming) SQL and do not allow for much more.
There needs to be more to author a serious actions-on-data algorithm.
What features are needed for these tools to truly succeed?
Telecom's credit scoring system with ML inference
Using Nussknacker with MLflow, enabled data scientists to deploy ML models directly while letting business analysts manage credit rules. This resulted in faster updates, reduced developer dependency, and more efficient credit risk assessment for their 13 million customers.
Streaming SQL alternative
Many streaming applications require significant domain knowledge and continuous updates, however SQL is neither up to the task nor user-friendly for domain experts
Real-Time Recommendations: Using Machine Learning in Clickstream Processing Pipeline
Nussknacker simplifies the integration of machine learning models into streaming data processes. Software teams can now build intelligent recommendation systems using Nussknacker, Snowplow, and MLflow.
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Nussknacker can make your data processing use case more agile and easier to manage.