AI/ML Infrastructure
Designing, building, and maintaining the compute, storage, networking, and orchestration infrastructure that powers machine learning training and inference workloads.
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Why It Matters
Every AI application depends on reliable, scalable infrastructure; as AI adoption explodes, professionals who can provision and optimize GPU clusters, model serving, and data pipelines are in acute demand.
How to Get Started
Learn Kubernetes for ML workloads, get certified on AWS SageMaker or Google Vertex AI infrastructure, and practice deploying a model end-to-end with MLflow on cloud infrastructure.
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