pipeline▌

Orchestrates video generation across three engines, selecting the best one based on requirements and available resources.

End-to-end MLOps pipeline orchestration from data ingestion through model deployment and monitoring. \n \n Covers five core pipeline stages: data preparation, model training, validation, deployment, and monitoring with DAG orchestration patterns (Airflow, Dagster, Kubeflow) \n Includes data validation, feature engineering, experiment tracking integration, and model versioning strategies across the full ML lifecycle \n Provides deployment automation patterns including canary releases, blue-green

Intelligent model routing, budget tracking, and retry logic to optimize LLM API costs without sacrificing quality. \n \n Routes requests to cheaper models (Haiku) for simple tasks and expensive models (Sonnet, Opus) only when complexity thresholds are met, reducing spend by 3–19x on routine work \n Tracks cumulative API costs with immutable dataclasses, enforces budget limits, and fails early to prevent overspend \n Implements narrow retry logic that retries only on transient errors (network, ra