Kserve Deployment for Scalable Inference in Kubernetes

Course overview

Kserve Deployment for Scalable Inference in Kubernetes 1. Introduction to Kserve for scalable inference: Definition and fundamentals of Kserve, importance of scalable inference in Kubernetes, overview of Kserve architecture 2. Installation and configuration of Kserve: Step-by-step guide to installation, configuring Kserve for different models, best practices for integration into Kubernetes 3. Model development and deployment with Kserve: Creating and training models for Kserve, deployment strategies for machine learning models, monitoring and maintenance of models in Kserve 4. Utilizing advanced features of Kserve: Using multi-model serving for efficiency, integrating A/B testing and rollbacks, optimizing inference times and resource usage 5. Management of inference solutions in Kubernetes: Strategies for enterprise implementation, coordination between DevOps and data science teams, leading innovation projects in the field of inference 6. Performance analysis of inference solutions: KPIs for evaluating inference performance, methods for continuous optimization, integrating user feedback into the improvement process 7. Practical project for implementing Kserve: Developing your own inference project with Kserve, presenting results and solutions, critical feedback and discussion within the team 8. Reflection on the use of Kserve: Lessons learned from practical application, future perspectives for inference solutions in Kubernetes, strategies for continuous improvement and innovation

Upcoming dates

• 2026-09-14T22:00:00.000Z · 2027-05-03T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-09-14T22:00:00.000Z · 2027-11-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-09-14T22:00:00.000Z · 2027-05-03T22:00:00.000Z · München · Combined Learning
• 2026-09-14T22:00:00.000Z · 2027-11-22T23:00:00.000Z · München · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-06-13T22:00:00.000Z · Virtuelles Klassenzimmer