Apache Kafka and Machine Learning / Deep Learning in Banking and Finance Industry. See use cases, architectures (hybrid, cloud, edge) and an example for 24/7 fraud detection in real time at scale.


This session explores how and why Apache Kafka has become the de facto standard for reliable and scalable streaming infrastructures in the banking sector and financial services.



AI / Machine learning and the Apache Kafka ecosystem are a great combination for training, deploying and monitoring analytic models at scale in real time. They are showing up more and more in projects, but still feel like buzzwords and hype for science projects.


See how to connect the dots!
- How are Kafka and Machine Learning related?
- How can they be combined to productionize analytic models in mission-critical and scalable real time applications?

- We will discuss a step-by-step approach to build a scalable and reliable real time infrastructure for fraud detection in an instant-payment application using Deep Learning and an Autoencoder for anomaly detection


We build a hybrid architecture using technologies such as Apache Kafka, Kafka Connect, Kafka Streams, ksqlDB, TensorFlow, TF Serving, TF IO, Confluent Tiered Storage, Google Cloud Platform (GCP), Google Cloud Storage (GCS), and more.


Blog Post: [ Ссылка ]


Slides: [ Ссылка ]

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