 Название: Scalable Kubernetes Infrastructure for AI Platforms: Kubernetes-Native Training and Deployment
Автор: Alex Corvin, Taneem Ibrahim, Kyle Stratis
Издательство: O’Reilly Media, Inc.
Год: 2025-02-13
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB
Generative AI is transforming industries, but for many enterprises, the journey from proof of concept to production remains a major hurdle. While businesses are investing heavily in building AI-powered applications like RAG-based chatbots, the vast majority of these projects fail to deliver tangible results. Success demands more than experimentation—it requires a deeper understanding of the challenges of managing AI in production and adopting MLOps practices to streamline the process.
This report explores how enterprises can leverage MLOps, with a Kubernetes-first approach, to overcome adoption barriers, scale AI effectively, and maximize business impact. From building responsible models to running reliable production systems, our guide offers the strategies and tools you need to thrive in an AI-driven competitive landscape.
Addressing and overcoming these challenges is at the core of the relatively recent discipline of Machine Learning Operations (MLOps). This publication will walk you through why this is a critical next step and how to leverage MLOps on Kubernetes. In this report, we’ll unpack four fundamentals of building AI-powered applications:
• Training models in the experimental phase
• Making model creation repeatable and declarative
• Operating models in production as a part of AI-powered applications
• Ensuring that models you create are trustworthy and built responsibly
This report will take a Kubernetes-centric approach, highlighting projects that are built to be Kubernetes native and when used together allow you to apply MLOps principles and practices to building AI-powered applications.
MLOps has its origin in the world of DevOps, a best-practice development model that seeks to deliver high-quality software to production quickly. It seeks to do this by bringing development and operations roles closer together. This fosters collaboration and shared knowledge across the software development and production lifecycles while bringing awareness of production issues to the teams and individuals best equipped to solve them. This approach requires that developers concern themselves with how the software they write performs in production and that they’re actively involved in operating that software in production as well.
With the proliferation of AI/ML and an ever-increasing number of models being created, MLOps has emerged as a new paradigm for delivering high-quality models to production quickly, applying DevOps principles to AI models and AI-powered applications instead of traditional software applications. However, MLOps doesn’t just apply DevOps principles to the AI development lifecycle but builds upon them to define foundational best practices for building and running AI-powered applications.
• Accelerate AI projects from experimentation to production readiness
• Standardize and streamline model creation for repeatability
• Deploy and manage AI models in production with confidence
• Build trust by creating responsible, explainable AI systems
• Leverage Kubernetes-native tools to apply MLOps principles at scale
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