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TERRAFORM
Provision and manage infrastructure as declarative code
BUSL-1.1
ABOUT
Setting up AI/ML infrastructure — GPU clusters, vector databases, model serving endpoints, data pipelines — requires manual cloud console steps that are error-prone, unrepeatable, and hard to audit. Terraform solves this by letting teams define their entire infrastructure as declarative configuration files that can be version-controlled, peer-reviewed, and automatically applied across clouds, ensuring that training environments, deployment targets, and supporting services are always provisioned consistently and reproducibly.
INTEGRATION GUIDE
1. Provision GPU compute clusters with attached storage, networking, and IAM policies for distributed ML training
2. Deploy and manage vector databases, model registries, and feature stores as infrastructure with automated scaling
3. Create reproducible environments for ML experimentation with consistent cloud resource configurations
4. Manage multi-cloud AI infrastructure spanning AWS, GCP, and Azure from a single declarative configuration
5. Automate deployment of model serving infrastructure with load balancers, auto-scaling groups, and monitoring
TAGS
infrastructure-as-codeclouddevopsautomationprovisioningmlops