Autorentext

Prashanth Josyula is a seasoned IT professional based in San Francisco, USA, with over 16 years of industry experience spanning enterprise software engineering, artificial intelligence, and cloud-native infrastructure. He specializes in AI/ML systems, Kubernetes, MLOps, and service mesh technologies, and has consistently contributed to building intelligent, scalable, and resilient platforms that power next-generation applications.

In his current role as a Principal Member of Technical Staff (PMTS) at Salesforce, Prashanth is at the forefront of architecting cloud-native solutions that seamlessly integrate AI-driven automation, real-time data processing, and large-scale distributed systems. His work spans across platform services, ML infrastructure, and enterprise-grade deployments, enabling cross-functional teams to build, deploy, and manage intelligent applications with speed and reliability. Prashanth is also an active thought leader and speaker, regularly participating in and presenting at industry-leading conferences. His talks focus on advanced topics such as ML/AI Ops, Retrieval-Augmented Generation (RAG), AI Agents, Responsible AI, and Time-Series Forecasting, where he shares practical insights derived from real-world enterprise experience. With a strong passion for both innovation and knowledge-sharing, Prashanth combines deep technical expertise with a commitment to advancing the field through mentorship, public speaking, authorship, and contributions to research and open-source communities.

Klappentext

Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides solutions for real-world challenges.

With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

Inhalt

Part I: Foundation

1. Kubernetes Essentials for ML Engineers
2. Getting Started with Kubeflow

Part II: Building ML Workflows
3. Understanding Kubeflow Pipelines
4. Advanced Pipeline Development
5. Experimentation with Notebooks

Part III: Model Development and Training
6. Training at Scale
7. Hyperparameter Tuning with Katib

Part IV: Model Deployment
8. Serving Models with KServe
9. Production Operations

Part V: Enterprise Implementation
10. Production Best Practices
11. Platform Integration and Ecosystem