Machine Learning with Kafka on Kubernetes teaches data and tech professionals how to build rapidly responsive machine learning models at scale. The course runs over two comprehensive days. Participants learn how to build, monitor, and manage machine learning models using streaming data. This covers the strategies and tools needed to take a model from the whiteboard to production. Hands-on practice uses state-of-the-art technologies for streaming machine learning, including Kafka-ML, River, Kafka Streams, and FlinkML.
Day one grounds attendees in Kafka and Kubernetes fundamentals before moving into analyzing streaming data directly with Kafka Streams. The day closes with Flink and FlinkML for stream-native model training and inference. Day two shifts into building and deploying models. Spark MLlib trains at batch scale, while River demonstrates online learning, where a model updates itself continuously as new events arrive. Kafka-ML streams data straight into neural network training and serving. The course closes by evaluating and managing streaming models in production. It covers the monitoring and lifecycle concerns that come with a model that never stops learning.
Who Should Attend
Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing Machine Learning with Kafka on Kubernetes, participants will be able to:
- Build an in-depth understanding of how to build scalable streaming machine learning pipelines with Kafka and Kubernetes
- Learn how to work with online machine learning, models that can produce predictions and self-improve at the same time
- Gain hands-on experience using state-of-the-art technologies for streaming machine learning and analytics, such as Kafka-ML, River, Kafka Streams and FlinkML
- Develop the skills and expertise to build machine learning models that rapidly adapt to changing data and can produce continuous predictions
Prerequisites
Participants should have intermediate programming skills (preferably in Python), an understanding of mathematics and statistics, basic familiarity with machine learning / neural networks, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.