Phase 7: Hyperscalers & Cloud AI
Once you have a model, you need to deploy it reliably, scale it to handle traffic spikes, and monitor it in production. Cloud hyperscalers — AWS, Google, and Azure — provide managed infrastructure that makes this feasible without running your own data centre.
Deploy and scale AI workloads in production
6 – 8 weeks
AWS SageMaker · Vertex AI · Azure ML · Docker · Kubernetes
Why Cloud for AI?
Rent H100s by the hour instead of buying for $25,000+
Handle 1 or 1 million requests with the same infrastructure
No cluster management — focus on models, not infra
Serve predictions with low latency from data centres near your users
The Three Major Clouds
Amazon Web Services
Largest cloud provider. SageMaker is the most mature ML platform with end-to-end pipeline support.
- SageMaker — managed ML platform
- Bedrock — managed LLM APIs
- Rekognition — vision AI
- Comprehend — NLP services
Google Cloud Platform
Best TPU access, Vertex AI is deeply integrated with Google's AI research. Home of TensorFlow and Gemini.
- Vertex AI — unified ML platform
- TPUs — custom AI accelerators
- Gemini API — frontier LLMs
- BigQuery ML — SQL-based ML
Microsoft Azure
Deep OpenAI partnership. Best choice for enterprise teams already in the Microsoft ecosystem.
- Azure ML Studio — visual ML builder
- Azure OpenAI — GPT-4 API
- Cognitive Services — pre-built AI
- Fabric — data + AI platform
Comparing Cloud AI Platforms
🔄 MLOps & CI/CD for AI
Automate model training, testing, and deployment. Version control for models, data, and experiments.
The Cloud AI Deployment Checklist
Package model + dependencies in a Docker image for reproducibility
Use MLflow, DVC, or cloud model registries to track versions
REST API via SageMaker, Vertex AI, or custom Kubernetes
Track latency, throughput, and prediction drift in production
Trigger retraining when data drift exceeds a threshold
GPU instances can cost hundreds per hour — set billing alerts!
Frequently Asked Questions
Which cloud should I learn first?
AWS has the most job demand (largest market share). GCP if you're working with large-scale ML research or TPUs. Azure if your organisation is Microsoft-heavy. The concepts transfer between all three — learn one deeply, then the others will be familiar.
Is it cheaper to self-host vs cloud?
At low scale (<1B tokens/month), cloud APIs are cheaper (no infra management). At high scale (>10B tokens/month), self-hosting open-source models on rented or owned GPU servers typically wins. The crossover depends on your model size and utilisation rate.
What is MLOps and why do I need it?
MLOps (Machine Learning Operations) applies DevOps practices to ML: version control, CI/CD pipelines, automated testing, and monitoring for models. Without it, you end up with "model debt" — models that nobody knows how to retrain or update safely.
Frequently Asked Questions
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