AI/ML 工作負載的激增需要一個強大的基礎設施，它能夠提供可擴展性、高效的開發者服務和堅定的安全性。在本研討會中，我們將深入探討 Google Kubernetes Engine 如何幫助您構建這樣一個平台。

我們將探索：

靈活的基礎設施：利用強大的虛擬機、高性能存儲和無縫擴展能力來支持多種 AI/ML 工作負載。

簡化的 ML Ops：通過 Google Cloud 的工具和框架，在整個 ML 生命週期中實施最佳實踐，使您的專家能夠有效協作並加速模型開發和部署。

強大的安全性：使用 Google Cloud 的加密解決方案、精細控制和機密計算來保護您的寶貴數據，確保您的 AI 平台從端到端保持安全。

優化的資源利用率：通過根據您的需求量身定制的消費模式和有助於消除資源浪費的工具，最大限度地提高性價比。

The surge in AI/ML workloads demands a robust infrastructure that offers scalability, efficient developer services, and unwavering security. In this session, we'll delve into how Google Kubernetes Enginee empowers you to build such a platform.

We'll explore:

Flexible Infrastructure: Leverage powerful virtual machines, high-performance storage, and seamless scaling capabilities to support diverse AI/ML workloads.

Streamlined ML Ops: Implement best practices throughout the ML lifecycle with Google Cloud's tools and frameworks, enabling your experts to collaborate effectively and accelerate model development and deployment.

Robust Security: Safeguard your valuable data with Google Cloud's encryption solutions, granular controls, and Confidential Computing, ensuring your AI platform remains secure from end to end.

Optimized Resource Utilization: Maximize price-performance with consumption models tailored to your needs and tools that help eliminate resource wastage.

目標聽眾：雲端架構師和工程師、資料科學家和機器學習工程師、DevOps 工程師