We can't just adopt new tools; we need to combine new tools with a mindset shift.

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We can't just adopt new tools; we need to combine new tools with a mindset shift.

Uniting ML and data streaming requires overcoming technical and socio-technical barriers. Organisations need to adopt decentralised, domain-driven approaches like data mesh and feature-oriented teams. Utilising Apache Kafka as the backbone for real-time data integration can operationalise ML by providing the speed and scalability required for real-time analytics. This combined approach enables organisations to leverage real-time ML effectively, driving innovation and responsiveness.

We can't just adopt new tools; we need to combine new tools with a mindset shift.
We can't just adopt new tools; we need to combine new tools with a mindset shift.

Uniting ML and data streaming requires overcoming technical and socio-technical barriers. Organisations need to adopt decentralised, domain-driven approaches like data mesh and feature-oriented teams. Utilising Apache Kafka as the backbone for real-time data integration can operationalise ML by providing the speed and scalability required for real-time analytics. This combined approach enables organisations to leverage real-time ML effectively, driving innovation and responsiveness.

https://www.confluent.io/blog/uniting-machine-learning-data-streaming-1/?utm_campaign=tm.content_devx-resource-roundup-ai-ml&utm_medium=blogpost

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