How We Built Our AI Lab: A Practical Walkthrough - AI-Xplore webinar series

When it comes to AI and GenAI, the quality and performance of your models are only as good as the infrastructure they’re built on. Unlike traditional software applications, AI models are incredibly resource-intensive.

While cloud resources provide easy access to powerful computing tools and promise scalability, they often come with hidden costs, performance variability, and latency issues. With a physical setup, you gain complete control over your hardware and software environment, allowing consistent, high-performance computing. At InfraCloud, we recognized these benefits early on, which is why we invested in building our own AI lab.

For AI experimentation, prototyping, and innovation, an AI lab is a no-brainer, but setting it up could be a mess. You may face challenges around hardware/software environments, compatibility, and data management. We used Kubernetes to simplify the management of multiple AI models and workloads running simultaneously across different environments. We also leveraged other cloud native technologies to enhance the observability, scalability, and reliability of the AI lab.

In this webinar, Sanket, Vishal, and Atul will give you a practical walkthrough of building an AI lab, drawing from their hands-on experience in setting one up.

Key takeaways 🎯
Importance of infrastructure for AI apps: Learn the importance of infrastructure in making any AI application.
Hardware and software: Learn about the specialized hardware and software environments required to develop infrastructure to make AI apps.
Kubernetes & cloud native technologies: Discover how various tools and technologies help develop, run, and manage AI software.
Tools & technologies: We’ll reveal the specific AI frameworks, libraries, and tools we used, including Kubernetes, Docker, and observability-related technologies.
Live demonstration: Watch a live demo where our experts showcase the system configuration, infrastructure deployment using IaC, Kubernetes setup, and how to deploy GPU-intensive applications.
Challenges & solutions: We will share the challenges we face and how we found their solutions so you can have valuable insights for your AI Lab journey.
Plans: Get a sneak peek into our upcoming plans for the AI Lab.

Speakers of this webinar:
Atulpriya Sharma (Sr. Dev Advocate @ InfraCloud)
Sanket Sudake (Principal Engineer @ InfraCloud)
Vishal Biyani (CTO & Founder @ InfraCloud)

⌚Timestamps
0:00 Welcome and speaker introduction
5:38 GPU Cloud: what makes a GPU Cloud?
11:58: There is no artificial intelligence without an intelligent network
12:40 GPU Cloud - Networking
16:54 GPU Cloud - Networking Considerations
19:30 GPU Cloud - Benchmarks
20:50 GPU Cloud - Storage
21:09 GPU Cloud - Storage Considerations
23:06 GPU Cloud - Accelerators
26:00 InfraCloud AI Lab
27:22 AI Lab - Miniature GPU Cloud
29:05 AI Lab - Software Stack: Platform
36:27 InfraCloud Open source AI Helm Charts
37:19 AI Lab Demo
43:54 Build Your Own AI Lab
44:11 Live Q&A
54:24 Summing it up

Building your own AI lab? Use our AI stack charts on a Kubernetes cluster to setup your own AI lab: https://github.com/infracloudio/chart...
Prefer reading? Check out our blogs here: https://www.infracloud.io/blogs/categ...
Ready to Transform & Build Your AI Cloud? Elevate your org's AI & GPU cloud capabilities with tailored consulting and management services: https://www.infracloud.io/build-ai-cl...