Optimal Approaches for Real-Time Machine Learning with Apache Spark on Kubernetes: Best Practices and Strategies
With the increasing integration of machine learning (ML) models into modern applications, the demand for deploying them in real-time environments has grown significantly. Apache Spark, a widely-used open-source framework for large-scale data processing, offers support for ML tasks, while Kubernetes provides a robust platform for container orchestration and deployment. However, combining Spark and Kubernetes presents notable challenges, particularly in achieving low latency and high scalability.
This presentation explores optimal approaches for real-time ML with Apache Spark on Kubernetes. It covers a range of topics, including best practices and strategies for efficient model training, deployment, and serving. Key considerations such as resource management, containerization, and monitoring are discussed, along with practical tips for optimizing ML workflows using Spark and Kubernetes. By following these guidelines, developers and data scientists can enhance the performance and scalability of their real-time ML applications.