KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

In the competitive AI market, Time To Market (TTM) is crucial for success. Ensuring secure, scalable, and compliant ML infrastructures often slows TTM due to the complexities of updates, patches, monitoring, and security enforcement. This leads to decreases in ROI, profitability, reproducibility, and competitive edge. To address this, companies can engage Managed Service Providers (MSPs) to offload operational burdens and focus on innovation, yet selecting the right MSP requires consideration of expertise, automation capabilities, and compliance adherence. This presentation explores the AI operational landscape, highlighting indicators and challenges in MSP collaboration. We will focus on the management of open source tools like Kubeflow and MLflow across hybrid and multicloud environments. By understanding operational excellence in AI and available options to achieve it, attendees will gain insights into choosing an approach that aligns with their greater objectives.

在竞争激烈的人工智能市场中，上市时间对于成功至关重要。确保安全、可扩展和合规的机器学习基础设施通常会因更新、补丁、监控和安全执行的复杂性而减慢上市时间，导致投资回报率、盈利能力、可复制性和竞争优势下降。为了解决这个问题，公司可以与托管服务提供商（MSPs）合作，减轻运营负担，专注于创新，但选择合适的MSP需要考虑专业知识、自动化能力和合规性。 本次演讲探讨了人工智能运营领域，重点介绍了MSP合作中的指标和挑战。我们将重点关注在混合和多云环境中管理开源工具如Kubeflow和MLflow。通过了解人工智能运营卓越性以及实现卓越性的可用选项，与会者将获得选择与其更大目标一致的方法的见解。