feat(deploy): Dockerfile + compose for api + mlflow server

.dockerignore

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+.venv/
+.venv312/
+__pycache__/
+*.pyc
+.pytest_cache/
+.mypy_cache/
+data/raw/*
+data/processed/*
+mlruns/
+.git/
+docs/
+tests/

Dockerfile

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+# NeuroBridge Enterprise — multi-stage build, FastAPI + pipeline runtime image.
+# Python 3.12 because RDKit / scikit-learn / numpy pins ship cp310-cp312 wheels only.
+FROM python:3.12-slim AS runtime
+
+# System deps required by RDKit (libxrender, libxext) and nibabel/MNE
+# (libgomp). Slim base lacks them.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libxrender1 \
+    libxext6 \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install dependencies first so the layer is cached when only source changes.
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY src/ src/
+COPY AGENTS.md README.md ./
+
+# Determinism env vars baked in (the pipelines re-pin defensively but
+# baking them avoids a brief race on container start).
+ENV OMP_NUM_THREADS=1 \
+    OPENBLAS_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    PYTHONUNBUFFERED=1
+
+EXPOSE 8000
+CMD ["uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]

docker-compose.yml

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+services:
+  mlflow:
+    image: ghcr.io/mlflow/mlflow:v2.16.0
+    command: >
+      mlflow server
+      --host 0.0.0.0
+      --port 5000
+      --backend-store-uri /mlflow/mlruns
+      --default-artifact-root /mlflow/mlruns
+    ports:
+      - "5000:5000"
+    volumes:
+      - mlflow-data:/mlflow/mlruns
+
+  api:
+    build: .
+    ports:
+      - "8000:8000"
+    environment:
+      MLFLOW_TRACKING_URI: http://mlflow:5000
+    depends_on:
+      - mlflow
+    volumes:
+      - ./data:/app/data
+
+volumes:
+  mlflow-data: