Ultimate Guide to AI Infrastructure

Learn about AI infrastructure, landscape, and ecosystem.

With hundreds of AI/ML infrastructure tools on the market, how do you make sense of it all? Which parts of the stack are mature? How do you know where to invest your precious time and resources to get the best results for your rapidly growing team of data engineers and data scientists?
At the AI Infrastructure Alliance, a non-profit organization dedicated to research and education, we recently delivered the most comprehensive report on the entire AI infrastructure ecosystem to help answer these very questions. We went way beyond marketing and dug deep into documentation, demos, and examples to understand the real capabilities of everything from orchestrators and pipelines to training, deployment, monitoring, security, and management platforms.
In this talk we deliver the essential insights from our research, giving technical execs and architects the keys they need to build or expand their infrastructure by providing a comprehensive and clear overview of the entire AI/ML infrastructure landscape.

Table of Contents:
0:00 – Introduction
0:15 – AI infrastructure ecosystem of 2022
2:47 – Choosing the right architecture
10:32 – Two types of orchestration
14:36 – Current trends
18:59 – Deployment and serving
20:04 – AI supervision
27:53 – Data versioning, lineage, metadata
39:45 – Synthetic data
49:48 – Q/A

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[firefoxmetzger]

Daniel needs to speak a bit slower. It's easy to follow the pace, but he starts to suffer from a dry throat and struggles speaking calmly. This makes it a bit hard to follow at times. Speaking slower should help prevent this.