AI Decoded: Implementation Strategies (Part 2)

1. Cloud vs Edge AI

One of the first decisions when implementing AI is where the models will run — on the cloud or at the edge. Each has its benefits:

• Cloud AI: Scalable, powerful, great for training large models.
• Edge AI: Low latency, privacy-preserving, ideal for IoT and real-time processing.

Explore more in-depth at Microsoft Azure Edge AI.

2. Cost-Effective AI Deployment

When deploying AI at scale, cost management becomes critical. Use frameworks such as:

• MLflow for model tracking and reuse
• Kubeflow for AI orchestration on Kubernetes

See real-world savings via Google Cloud’s Kubeflow use cases.

3. Hardware Benchmarks (2025 Update)

Top Performers

• NVIDIA H200: 2.5x faster for transformer training compared to A100
• AMD MI300X: Ideal for multi-modal generative models

Latest benchmark data: NVIDIA H200 Launch Report

4. Deployment Best Practices

• Use CI/CD pipelines for continuous training & deployment
• Monitor models post-deployment for drift and performance
• Containerize with Docker and orchestrate with Kubernetes

Get started with ML Exchange Deployment Tools.

Coming in Part 3: Generative AI and Foundation Models

• Overview of GPT-4, Claude, Gemini, Mistral
• Use cases across industries
• Fine-tuning and prompt engineering tips