AI Infra Summit Mountain View 2025

The AI Infra Summit 2025 is scheduled for May 2, 2025 in Mountain View, Calif. The event will convene industry leaders, AI developers, and technology experts to discuss advancements in AI infrastructure.

Attendees can expect thought-provoking keynotes, solution showcases, and networking opportunities aimed at advancing AI technology. The summit will feature speakers from organizations such as Inflection AI, Supermicro and Microsoft.

Key Themes of AI Infra Summit 2025

• Ecosystem Synergy and Collaboration: Exploring strategies for fostering partnerships between AI developers and infrastructure providers to enhance interoperability. Relevant for business development managers and partnership leads.
• Integration of Infrastructure with Applied AI: Discussing how robust infrastructure supports the deployment of AI applications across industries. Beneficial for AI engineers and system architects.
• Current Trends in AI Conversations: Analyzing prevailing discussions and future directions in AI technology. Suitable for technology strategists and innovation officers.

AI Infra Summit Target Audience

• Chief Technology Officers (CTOs): Overseeing technological strategies and infrastructure development.
• AI and Machine Learning Engineers: Developing and deploying AI models requiring scalable infrastructure.
• Data Center Managers: Managing facilities that support AI computational needs.
• Business Development Managers: Seeking partnerships in the AI technology sector.

AI Infrastructure Alliance: Current Offerings

• Enterprise Generative AI Adoption Report (Oct 2023): A comprehensive guide covering agents, large language models, and smart applications, focusing on emerging AI stacks and legal considerations.
• AI Infrastructure Landscape Graphic: An updated visual representation showcasing capabilities of major MLOps tools, aiding data engineering and data science teams in tool selection.
• Virtual Events and Summits: Hosting events featuring industry experts discussing topics like large language models, data-centric AI, and AI scalability.
• Blueprint Diagrams Repository: A collection of diagrams illustrating configurations of canonical stacks for machine learning, available for adaptation and use.