AI & MLOps
AI Infrastructure & MLOps Engineering
Run machine learning and LLM workloads in production: GPU scheduling, model serving, pipelines, monitoring and AI agents for operations.
Kubernetes
NVIDIA GPUs
MLflow
Hugging Face
Python- Duration
- 3–4 weeks
- Level
- Advanced
- Format
- Live online · Corporate onsite
Outcomes
What you will be able to do
Schedule and share GPUs across teams on Kubernetes
Serve open models and LLMs with autoscaling
Automate training and deployment with MLflow pipelines
Build an ops assistant with safe, approved actions
Curriculum
5 modules
- The ML lifecycle
- Where DevOps and MLOps differ
- Reference architectures
FAQ
Questions, answered
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Start AI & MLOps with FlickOps.
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