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It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. So why did Uber (and many other tech companies) build its own platform and framework-independent machine learning infrastructure? Uber expanded Michelangelo “to serve any kind of Python model from any source to support other Machine Learning and Deep Learning frameworks like PyTorch and TensorFlow. This structure worked well for production training and deployment of many models but left a lot to be desired in terms of overhead, flexibility, and ease of use, especially during early prototyping and experimentation. When Michelangelo started, the most urgent and highest impact use cases were some very high scale problems, which led us to build around Apache Spark (for large-scale data processing and model training) and Java (for low latency, high throughput online serving). Uber, which already runs their scalable and framework-independent machine learning platform Michelangelo for many use cases in production, wrote a good summary: This is important to note since machine learning is clearly gainin g steam, though many who use the term do so by misusing the term.
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It takes much more effort than just building an analytic model with Python and your favorite machine learning framework.Īfter all, machine learning with Python requires the use of algorithms that allow computer programs to constantly learn, but building that infrastructure is several levels higher in complexity. Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy.