Phase 4: Cloud AI Services
You don't have to build AI infrastructure from scratch. Every major cloud provider now offers managed services that let you train, deploy, and monitor models without worrying about hardware. This phase covers the key AI and ML services from AWS, Google, and Azure — and when to reach for each one.
Managed ML Platforms
AWS SageMaker, Google Vertex AI, and Azure ML side by side — what they offer, how their pricing works, and how to choose between them for your project.
Start here →AI APIs & Foundation Model Services
Using GPT-4, Claude, Gemini, and Llama via cloud APIs — the difference between API access, fine-tuning services, and hosting your own model.
Explore AI APIs →Vector Databases in the Cloud
Pinecone, Weaviate, Qdrant, pgvector — how vector databases power RAG applications, semantic search, and recommendation systems in cloud environments.
Learn vector databases →MLOps on Cloud
Model registries, training pipelines, A/B testing, and production monitoring — how to operationalize ML models using cloud-native MLOps tools.
Explore MLOps →Frequently Asked Questions
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