Radu Vunvulea
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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