Streamlining AI Pipelines With Elyra - From Development to Inference With KServe and VLLM

Explore how to streamline AI/ML pipelines using Elyra, an open source project that extends JupyterLab to simplify data science and AI model development. Learn how Elyra empowers data scientists and ML engineers to build, automate, and optimize end-to-end AI/ML pipelines through its visual pipeline editor and seamless integration with Kubeflow and other MLOps tools. Discover how to accelerate pipeline development using Jupyter notebooks, reusable components, and automated workflows while implementing best practices for MLOps automation including model training, deployment, and monitoring. Dive into optimizing model inference with KServe for scalable, production-ready model serving and vLLM for high-performance large language model inference with optimized GPU utilization. Gain practical insights into workflow orchestration, unified inference platforms, and efficient LLM deployment strategies for building robust AI systems from development through production inference.