Summary: Bridge DevOps practices with AI/ML workflows for scalable deployments.
Responsibilities:
- Automate infrastructure provisioning for AI workloads.
- Integrate ML pipelines into existing DevOps frameworks.
- Ensure security and compliance of AI systems.
Skills: - Expertise in Terraform, Jenkins, and GitOps.
- Familiarity with AI/ML frameworks and GPU optimization.
Key Process: Bridging AI Workflows with DevOps Practices
- Inputs: AI/ML code, infrastructure requirements, security policies.
- Activities:
- Automate provisioning of GPU/TPU clusters.
- Secure AI pipelines (e.g., data encryption, access controls).
- Optimize resource allocation for training/inference.
- Outputs: Scalable AI infrastructure, audit trails, cost reports.
- Stakeholders: Cloud architects, security teams, AI developers.
- Tools: Terraform, Jenkins, Docker.