Radu Vunvulea

Reference Architecture of AI in the Cloud

Adding AI is more than a new service. It demands a foundational modernization of your entire cloud architecture, from data lakes to MLOps.

Reference Architecture of AI in the Cloud
#1about 2 minutes

Preparing existing cloud applications for AI integration

Many existing cloud applications are not ready for AI, similar to how a classic car is not ready for an electric charger.

#2about 5 minutes

Navigating the complex landscape of AI cloud services

Cloud vendors offer a rapidly changing and complex array of AI services, requiring continuous learning to select the right tools.

#3about 5 minutes

Overcoming key challenges in cloud AI adoption

Integrating AI requires addressing challenges like application performance degradation, data silos, compliance versus innovation, and managing costs.

#4about 6 minutes

Core pillars for a successful AI implementation

A successful AI integration depends on modernizing applications with auto-scaling, unified data platforms, mature CI/CD pipelines, and robust observability.

#5about 5 minutes

Essential cloud services for building AI architectures

Key services like Kubernetes, serverless functions, data fabrics, API gateways, and data catalogs form the foundation of a robust AI architecture.

#6about 3 minutes

Understanding the full scope of an AI solution

A reference architecture diagram reveals that AI services are only a small component of a complete solution, which requires extensive supporting infrastructure.

#7about 2 minutes

A step-by-step flow for AI modernization

Follow a structured modernization process focusing on compute, data, and DevOps before integrating AI, and finish by adding comprehensive observability.

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