MLOps & LLMOps

Operationalize machine learning and large language models by unifying CI/CD workflows with automated data validation and model monitoring.

Modern MLOps and LLMOps bridge the gap between experimental notebooks and production scale. We implement robust pipelines using tools like Kubeflow for orchestration and MLflow for experiment tracking to ensure 99.9% reliability. For LLMs, we integrate vector databases (Pinecone or Milvus) and evaluation frameworks (RAGAS) to manage prompt versioning and prevent hallucinations. By automating the feedback loop from deployment back to retraining, we reduce deployment cycles from weeks to minutes while maintaining strict governance over model drift and token costs.

https://ml-ops.org/