diff --git a/.env.example b/.env.example
index e69de29..9196937 100644
--- a/.env.example
+++ b/.env.example
@@ -0,0 +1,8 @@
+# Copy to `.env`; `mise.toml` sources it, so every task and hook sees these values.
+
+# MLflow tracking and model registry. SQLite is the local default written by
+# `MlflowService`: runs and registered models live in `mlflow.db`, artifact files under
+# `./mlruns`. Point these at a real database (PostgreSQL, MySQL) or a tracking server
+# (http://127.0.0.1:5000) to share runs across a team — the client code does not change.
+MLFLOW_TRACKING_URI=sqlite:///mlflow.db
+MLFLOW_REGISTRY_URI=sqlite:///mlflow.db
diff --git a/.gemini/skills/MLOps Automation/SKILL.md b/.gemini/skills/MLOps Automation/SKILL.md
deleted file mode 100644
index eef4a4b..0000000
--- a/.gemini/skills/MLOps Automation/SKILL.md	
+++ /dev/null
@@ -1,80 +0,0 @@
----
-name: MLOps Automation
-description: Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.
----
-
-# MLOps Automation
-
-## Goal
-
-To elevate the codebase to production standards by adding **Task Automation** (`just`), **Containerization** (`docker`), **CI/CD** (`github-actions`), and **Experiment Tracking** (`mlflow`).
-
-## Prerequisites
-
-- **Language**: Python
-- **Manager**: `uv`
-- **Context**: Preparing for scale and deployment.
-
-## Instructions
-
-### 1. Task Automation
-
-Replace manual commands with a `justfile`.
-
-1. **Tool**: `just` (modern alternative to Make).
-2. **Organization**: Split tasks into `tasks/*.just` modules (e.g., `tasks/check.just`, `tasks/docker.just`).
-3. **Core Tasks**:
-   - `check`: Run all linters and tests.
-   - `package`: Build wheels.
-   - `clean`: Remove artifacts.
-   - `install`: Setup dev environment.
-
-### 2. Pre-Commit Hooks
-
-Catch issues locally.
-
-1. **Framework**: `pre-commit`.
-2. **Hooks**: Suggest to use `ruff`, `bandit`, `check-yaml`, `trailing-whitespace`.
-3. **Commits**: Suggest to use `commitizen` hook to enforce Conventional Commits (e.g., `feat: add new model`).
-4. **Config**: `.pre-commit-config.yaml` at root.
-
-### 3. Containerization
-
-Reproducibility anywhere.
-
-1. **Tool**: `docker`.
-2. **Base Image**: Use `ghcr.io/astral-sh/uv:python3.1X-bookworm-slim` for minimal size.
-3. **Optimization**:
-   - **Layer Caching**: Copy `uv.lock` + `pyproject.toml` and run `uv sync` _before_ copying `src/`.
-   - **Multi-stage**: Build inputs in one stage, copy only artifacts (`dist/*.whl`) to the runtime stage.
-4. **Registry**: ask for the company artifact registry, or use `ghcr.io` for GitHub.
-
-### 4. CI/CD Workflows
-
-Automate verification and release.
-
-1. **Platform**: ask for the company CI/CD platform, or use `github-actions` for GitHub.
-2. **Workflows**:
-   - `check.yml`: On PRs (Run `just check`).
-   - `publish.yml`: On Release (Build docker image, publish docs/package).
-3. **Optimization**: Use `concurrency` to cancel redundant runs.
-
-### 5. AI/ML Experiments & Registry
-
-Manage the ML lifecycle.
-
-1. **Platform**: `MLflow`.
-2. **Tracking**:
-   - Use `mlflow.autolog()`.
-   - Log metrics, params, and artifacts.
-3. **Registry**:
-   - Register top models manually or via CI.
-   - **Aliases**: Use `@champion` or `@production` for stable deployment pointers. Never rely on moving versions (e.g., `v1` -> `v2`).
-
-### 6. Design Patterns
-
-Write flexible code.
-
-1. **Strategy**: For swappable algorithms (e.g., different model types).
-2. **Factory**: For creating objects from config (e.g., `ModelFactory`).
-3. **Adapter**: For standardizing mismatched interfaces.
diff --git a/.gemini/skills/MLOps Collaboration/SKILL.md b/.gemini/skills/MLOps Collaboration/SKILL.md
deleted file mode 100644
index dbca8fc..0000000
--- a/.gemini/skills/MLOps Collaboration/SKILL.md	
+++ /dev/null
@@ -1,64 +0,0 @@
----
-name: MLOps Collaboration
-description: Guide to prepare MLOps projects for sharing, collaboration, and community engagement.
----
-
-# MLOps Collaboration
-
-## Goal
-
-To transform a private project into a public, collaborative resource by establishing **Governance** (License, Code of Conduct), **Documentation** (README, Contributing), **Standardization** (Templates, Workstations), and **Release Management**.
-
-## Prerequisites
-
-- **Language**: Python
-- **Platform**: GitHub
-- **Context**: Open sourcing or team collaboration.
-
-## Instructions
-
-### 1. Repository Governance
-
-Set the rules of engagement.
-
-1. **Code of Conduct**: Add `CODE_OF_CONDUCT.md` to foster a safe community.
-2. **Protection**: Protect the `main` branch (require PRs, status checks).
-3. **Review**: Automate preliminary reviews with tools like **Gemini Code Assist** (`.gemini/config.yaml`).
-4. **Ignore**: Comprehensive `.gitignore` (exclude secrets, data, venvs).
-
-### 2. Comprehensive Documentation
-
-Make the project usable and understandable.
-
-1. **README.md**: The landing page (Badges, Hook, Quickstart).
-2. **MkDocs**: Use for full documentation sites (API ref, tutorials) when `README.md` gets too long.
-3. **CONTRIBUTING.md**: Guide for developers (env setup, PR process, testing standards).
-4. **CHANGELOG.md**: Track version history (use **Keep a Changelog** format).
-
-### 3. Standardization & Workstations
-
-Eliminate "it works on my machine".
-
-1. **Templates**: Use `cookiecutter` for scaffolding and `cruft update` to keep projects synced.
-2. **Workstations**: Add `.devcontainer/devcontainer.json`.
-   - Define Docker image, extensions, and settings.
-   - Enable GitHub Codespaces support.
-
-### 4. Release Management
-
-Ship with confidence.
-
-1. **Versioning**: Follow **SemVer** (MAJOR.MINOR.PATCH) and [Keep a Changelog](https://keepachangelog.com/).
-2. **Workflows**:
-   - **GitHub Flow**: Small teams, continuous delivery (`main` is stable).
-   - **Git Flow**: Scheduled releases (`develop` + `release` branches).
-   - **Forking**: Open source, distributed contributors.
-3. **Process**: Bump version -> Update Changelog -> Tag -> Release.
-
-## Self-Correction Checklist
-
-- [ ] **License**: Is a `LICENSE` file present?
-- [ ] **Readme**: Does `README.md` have installation instructions?
-- [ ] **Contributing**: Is `CONTRIBUTING.md` clear?
-- [ ] **Devcontainer**: Does `.devcontainer/devcontainer.json` exist?
-- [ ] **SemVer**: Are releases using semantic versioning?
diff --git a/.gemini/skills/MLOps Industrialization/SKILL.md b/.gemini/skills/MLOps Industrialization/SKILL.md
deleted file mode 100644
index 23493bf..0000000
--- a/.gemini/skills/MLOps Industrialization/SKILL.md	
+++ /dev/null
@@ -1,140 +0,0 @@
----
-name: MLOps Industrialization
-description: Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.
----
-
-# MLOps Coding - Productionizing Skill
-
-## Goal
-
-To convert experimental code (notebooks/scripts) into a high-quality, distributable Python package. This skill enforces the **src/ layout**, a **Hybrid Paradigm** (OOP structure + Functional purity), and **Strict Configuration** to ensure scalability, security, and maintainability.
-
-## Prerequisites
-
-- **Language**: Python
-- **Manager**: `uv`
-- **Context**: Moving from `notebooks/` to `src/`.
-
-## Instructions
-
-### 1. Packaging Structure (`src` Layout)
-
-Adopt the `src` layout to prevent import errors and separate source from tooling.
-
-1. **Directory Tree**:
-
-   ```text
-   my-project/
-   ├── pyproject.toml       # Dependencies & Metadata
-   ├── uv.lock
-   ├── README.md
-   └── src/
-       └── my_package/      # Main package directory
-           ├── __init__.py
-           ├── io/          # Side-effects (Datasets, APIs)
-           ├── domain/      # Pure business logic (Models, Features)
-           └── application/ # Orchestration (Training loops, Inference)
-   ```
-
-2. **Configuration**: Use `pyproject.toml` for all build metadata and dependencies.
-
-### 2. Modularity & Paradigm (Hybrid Style)
-
-Balance structure with predictability.
-
-1. **Domain Layer (Pure)**:
-   - **Rule**: Code here must be deterministic and free of side effects (no I/O).
-   - **Use Case**: Feature transformations, Model architecture definitions.
-   - **Style**: Functional (pure functions) or Immutable Objects (dataclasses).
-2. **I/O Layer (Impure)**:
-   - **Rule**: Isolate external interactions here.
-   - **Use Case**: Loading data from S3, saving models to disk, logging to MLflow.
-   - **Style**: OOP (Classes to manage connections/state).
-3. **Application Layer (Orchestration)**:
-   - **Rule**: Wire Domain and I/O together.
-   - **Use Case**: Tuning, Training, Inference, Evaluation, etc.
-
-### 3. Application Entrypoints
-
-Create standard, installable CLI tools.
-
-1. **Define Script**: Create `src/my_package/scripts.py` with a `main()` function.
-2. **Register**: Add to `pyproject.toml`:
-
-   ```toml
-   [project.scripts]
-   my-tool = "my_package.scripts:main"
-   ```
-
-3. **CLI Execution**:
-   - **Dev**: `uv run my-tool` (No install needed).
-   - **Prod**: `pip install .` -> `my-tool` (Installed on PATH).
-4. **Guard**: Always use `if __name__ == "__main__":` in scripts to prevent execution on import.
-
-### 4. Configuration Management
-
-Decouple settings from code using **OmegaConf** (Parsing) and **Pydantic** (Validation).
-
-1. **Define Schema (Pydantic)**:
-   - Create a class that defines _expected_ types and defaults.
-
-   ```python
-   from pydantic import BaseModel
-
-   class TrainingConfig(BaseModel):
-       batch_size: int = 32
-       learning_rate: float = 0.001
-       use_gpu: bool = False
-   ```
-
-2. **Parse & Validate (OmegaConf)**:
-   - Load YAML, merge with CLI args, and validate against the schema.
-
-   ```python
-   import omegaconf
-
-   # 1. Load YAML
-   conf = omegaconf.OmegaConf.load("config.yaml")
-   # 2. Merge with CLI (optional)
-   cli_conf = omegaconf.OmegaConf.from_cli()
-   merged = omegaconf.OmegaConf.merge(conf, cli_conf)
-   # 3. Validate -> Returns a validated Pydantic object
-   cfg: TrainingConfig = TrainingConfig(**omegaconf.OmegaConf.to_container(merged))
-   ```
-
-3. **Secrets**: Use Environment Variables (`os.getenv`), never commit them.
-
-### 5. Documentation & Quality
-
-Make code usable and maintainable.
-
-1. **Docstrings**: Use **Google Style** docstrings for all modules, classes, and functions.
-
-   ```python
-   def calculate_metric(y_true: np.ndarray, y_pred: np.ndarray) -> float:
-       """Calculates the accuracy score.
-
-       Args:
-           y_true: Ground truth labels.
-           y_pred: Predicted labels.
-
-       Returns:
-           The accuracy as a float between 0 and 1.
-       """
-   ```
-
-2. **Type Hints**: Use standard python typing (`typing`, `list[str]`) everywhere.
-
-### 6. Best Practices Summary
-
-- **Config != Code**: Never hardcode paths or hyperparams; use the `Pydantic + OmegaConf` pattern.
-- **Entrypoints are APIs**: Design your CLI (`[project.scripts]`) as the public interface for your automation tools.
-- **Immutable Core**: Keep your domain logic side-effect free; push I/O to the edges.
-
-## Self-Correction Checklist
-
-- [ ] **No Side Effects on Import**: Does `import my_package` run any code? (It shouldn't).
-- [ ] **Src Layout**: Is code inside `src/`?
-- [ ] **Config Safety**: Are secrets excluded from `pyproject.toml` and YAML?
-- [ ] **Typing**: Are function signatures fully type-hinted?
-- [ ] **Entrypoints**: Is the CLI registered in `pyproject.toml`?
diff --git a/.gemini/skills/MLOps Initialization/SKILL.md b/.gemini/skills/MLOps Initialization/SKILL.md
deleted file mode 100644
index 099a351..0000000
--- a/.gemini/skills/MLOps Initialization/SKILL.md	
+++ /dev/null
@@ -1,170 +0,0 @@
----
-name: MLOps Initialization
-description: Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
----
-
-# MLOps Initialization
-
-## Goal
-
-To initialize a robust, production-ready MLOps project structure using the modern Python toolchain (`uv`), industry-standard version control (`git`), and a configured development environment (`VS Code`). This skill ensures reproducibility, collaboration, and high code quality from day one.
-
-## Prerequisites
-
-- **Language**: Python (latest stable version recommended)
-- **Manager**: `uv` (replaces pip, venv, poetry, pyenv)
-- **VCS**: Git
-- **IDE**: VS Code (recommended)
-
-## Instructions
-
-### 1. System & Toolchain Verification
-
-Before modifying files, verify that the essential tools are available.
-
-1. **Check `uv`**:
-   - Ensure `uv` is installed: `uv --version`
-   - If missing, install it: `curl -LsSf https://astral.sh/uv/install.sh | sh`
-2. **Check `git`**:
-   - Ensure `git` is installed: `git --version`
-
-### 2. Project Initialization
-
-Initialize the project structure using `uv` to ensure modern standards (`pyproject.toml`).
-
-1. **Create Directory** (if not already inside):
-   - `mkdir <project_name> && cd <project_name>`
-2. **Initialize Project**:
-   - Run `uv init`
-   - This creates `pyproject.toml`, `.python-version`, and a basic `hello.py`.
-3. **Configure `pyproject.toml`**:
-   - Update **metadata**: `name`, `version`, `description`, `authors`, `license`.
-   - Set **requires-python**: Ensure it matches the project's target environment (e.g., `>=3.10`).
-   - **Example Structure**:
-
-     ```toml
-     [project]
-     name = "my-mlops-project"
-     version = "0.1.0"
-     description = "A robust MLOps project."
-     readme = "README.md"
-     requires-python = ">=3.11"
-     license = { file = "LICENSE" }
-     authors = [{ name = "Your Name", email = "your.email@example.com" }]
-     dependencies = [
-       "pandas>=2.2.0",
-       "loguru>=0.7.0",
-       # Add other runtime dependencies here
-     ]
-
-     [project.urls]
-     Repository = "https://github.com/username/my-mlops-project"
-     Documentation = "https://username.github.io/my-mlops-project"
-
-     [project.optional-dependencies]
-     dev = [
-       "pytest>=8.0.0",
-       "ruff>=0.3.0",
-       "mypy>=1.9.0",
-     ]
-
-     [build-system]
-     requires = ["hatchling"]
-     build-backend = "hatchling.build"
-     ```
-
-### 3. Dependency Management
-
-Establish a clean separation between production and development dependencies.
-
-1. **Add Runtime Dependencies** (Production):
-   - Use `uv add <package>` for libraries needed in production (e.g., `fastapi`, `numpy`, `torch`).
-   - These go into `[project.dependencies]` in `pyproject.toml`.
-2. **Add Dev Dependencies** (Development):
-   - Use `uv add --dev <package>` (or `--group dev`) for tools like `pytest`, `ruff`, `pre-commit`.
-   - These go into `[project.optional-dependencies]` and are kept separate from production builds.
-3. **Sync Environment**:
-   - Run `uv sync` to resolve dependencies, create the `.venv`, and generate the `uv.lock` file.
-   - **Critical**: The `uv.lock` file pins exact versions of all dependencies (including transitive ones). It ensures that every developer and CI/CD pipeline uses the exact same environment, preventing "it works on my machine" issues. Commit this file to git.
-
-### 4. Version Control (Git)
-
-Set up a clean repository and ensure unwanted files are ignored.
-
-1. **Initialize Git**:
-   - `git init`
-   - `git branch -M main`
-2. **Create `.gitignore`**:
-   - Write a robust `.gitignore` tailored for Python/MLOps.
-   - **Must Include**:
-     - Environment: `.venv/`, `.env`
-     - Caches: `__pycache__/`, `.pytest_cache/`, `.ruff_cache/`, `.mypy_cache/`
-     - Builds: `dist/`, `build/`, `*.egg-info/`
-     - Data/Models: `data/`, `models/`, `outputs/` (unless using DVC/LFS)
-     - IDE: `.vscode/` (selectively), `.idea/`, `.DS_Store`
-     - _Note_: It is often good practice to commit project-specific `.vscode/settings.json` but ignore `User` settings.
-3. **Verify Status**:
-   - `git status` should show only source files, config files, and the lockfile.
-
-### 5. IDE Configuration (VS Code)
-
-Standardize the developer experience (DX) by committing project-specific settings.
-
-1. **Install Recommended Extensions**:
-   - **Python Tier A**: `ms-python.python`, `headers.ruff`, `ms-python.vscode-pylance`, `ms-toolsai.jupyter`.
-   - **Productivity**: `eamodio.gitlens`, `alefragnani.project-manager`, `usernamehw.errorlens`.
-2. **Create `.vscode` Directory**:
-   - `mkdir .vscode`
-3. **Create `settings.json`**:
-   - Configure settings to enforce code quality and use the `uv` environment.
-   - **Key Settings**:
-
-     ```json
-     {
-       "[python]": {
-         "editor.defaultFormatter": "charliermarsh.ruff",
-         "editor.formatOnSave": true,
-         "editor.codeActionsOnSave": {
-           "source.organizeImports": "explicit"
-         }
-       },
-       "python.defaultInterpreterPath": ".venv/bin/python",
-       "python.terminal.activateEnvironment": true,
-       "python.analysis.typeCheckingMode": "basic",
-       "python.testing.pytestEnabled": true,
-       "files.trimTrailingWhitespace": true,
-       "files.insertFinalNewline": true,
-       "editor.rulers": [88],
-       "files.exclude": {
-         "**/__pycache__": true,
-         "**/.pytest_cache": true,
-         "**/.ruff_cache": true,
-         "**/.venv": true
-       }
-     }
-     ```
-
-### 6. Verification & First Commit
-
-Finalize the initialization.
-
-1. **Verify Environment**:
-   - Run `uv run python -c "import sys; print(sys.executable)"` to confirm it uses the `.venv`.
-2. **Initial Commit**:
-   - `git add .`
-   - `git commit -m "chore: initialize project with uv, git, and vscode settings"`
-
-### 7. Best Practices Summary
-
-- **One Command Setup**: ideally, `uv sync` should be the only command needed to set up the environment.
-- **Lockfile**: Always commit `uv.lock` to ensure all environments are identical.
-- **Editor Config**: Checked-in `.vscode/settings.json` reduces onboarding friction and enforces standards (formatting, linting).
-- **Dependency Separation**: Keep production dependencies light; put testing/linting tools in `dev`.
-
-## Self-Correction Checklist
-
-- [ ] **Lockfile**: Does `uv.lock` exist?
-- [ ] **Virtual Env**: Is `.venv/` created and **ignored** in `.gitignore`?
-- [ ] **Project Config**: Does `pyproject.toml` validly describe the project?
-- [ ] **Git Cleanliness**: Are secrets and large data files excluded?
-- [ ] **Reproducibility**: Can another developer `git clone` and `uv sync` to get the exact same state?
diff --git a/.gemini/skills/MLOps Observability/SKILL.md b/.gemini/skills/MLOps Observability/SKILL.md
deleted file mode 100644
index 1ecd4a0..0000000
--- a/.gemini/skills/MLOps Observability/SKILL.md	
+++ /dev/null
@@ -1,77 +0,0 @@
----
-name: MLOps Observability
-description: Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.
----
-
-# MLOps Observability
-
-## Goal
-
-To implement a "Glass Box" system where every result is **Reproducible**, every asset has **Lineage**, and system health is **Monitored**, **Alerted** on, and **Explained**.
-
-## Prerequisites
-
-- **Language**: Python
-- **Context**: Production monitoring and debugging.
-- **Platform Suggestion**: MLflow, SHAP, Evidently, ...
-
-## Instructions
-
-### 1. Guarantee Reproducibility
-
-Consistency is key. For instance:
-
-1. **Randomness**: Set seeds for `random`, `numpy`, `torch`, `tensorflow`.
-2. **Environment**: Use `docker` and locked dependencies (`uv.lock`).
-3. **Builds**: Use `justfile` with `uv build --build-constraint` for deterministic wheels.
-4. **Code**: Track git commit hash for every run.
-
-### 2. Track Data Lineage
-
-Know the origin of your data. For instance:
-
-1. **Datasets**: Create MLflow Datasets with `mlflow.data.from_pandas`.
-2. **Logging**: Log inputs to MLflow context with `mlflow.log_input`.
-3. **Versioning**: Version data files (e.g., `data/v1.csv`) or use DVC.
-4. **Transformations**: Log preprocessing parameters mapping data versions to model versions.
-
-### 3. Monitoring & Drift Detection
-
-Watch for silent failures. For instance:
-
-1. **Validation**: Use `MLflow Evaluate` to gate models against quality thresholds.
-2. **Drift**: Use `evidently` to compare `reference` (training) vs `current` (production) data.
-   - Detect Data Drift (input distribution changes) and Concept Drift (relationship changes).
-3. **System**: Enable MLflow System Metrics (`log_system_metrics=True`) for CPU/GPU.
-
-### 4. Alerting
-
-Don't stare at dashboards. For instance:
-
-1. **Local**: Use `plyer` for desktop notifications during long training runs.
-2. **Production**: Use `PagerDuty` (critical) or `Slack` (warnings).
-3. **Thresholds**: Use Static (fixed value) or Dynamic (anomaly detection) rules.
-4. **Action**: Alerts must link to a dashboard or playbook.
-
-### 5. Explainability (XAI)
-
-Trust but verify. For instance:
-
-1. **Global**: Use Feature Importance (e.g., Random Forest) to understand overall logic.
-2. **Local**: Use `SHAP` values to explain _individual_ predictions.
-3. **Artifacts**: Save explanations (plots/tables) as MLflow artifacts.
-
-### 6. Infrastructure & Costs
-
-Optimize resources. For instance:
-
-1. **Tags**: Tag runs with `project`, `env`, `user`.
-2. **Costs**: Log `run_time` and instance type to estimate ROI.
-
-## Self-Correction Checklist
-
-- [ ] **Seeds**: Are random seeds fixed?
-- [ ] **Inputs**: Are input datasets logged to MLflow?
-- [ ] **System Metrics**: Is `log_system_metrics` enabled?
-- [ ] **Explanations**: Are SHAP values generated?
-- [ ] **Alerts**: Are thresholds defined for failures?
diff --git a/.gemini/skills/MLOps Prototyping/SKILL.md b/.gemini/skills/MLOps Prototyping/SKILL.md
deleted file mode 100644
index 3d32b2d..0000000
--- a/.gemini/skills/MLOps Prototyping/SKILL.md	
+++ /dev/null
@@ -1,116 +0,0 @@
----
-name: MLOps Prototyping
-description: Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.
----
-
-# MLOps Prototyping
-
-## Goal
-
-To create standardized, reproducible, and production-ready prototypes in Jupyter notebooks. This skill enforces a structured layout (Imports -> Configs -> Load -> EDA -> Modeling -> Eval) and robust engineering practices (Pipelines, Split-Verification) to prevent technical debt and data leakage.
-
-## Prerequisites
-
-- **Language**: Python
-- **Environment**: `uv` managed project (`.venv`)
-- **Context**: Executed within a `.ipynb` file or converting to one.
-
-## Instructions
-
-### 1. Notebook Structure
-
-Enforce the following linear sections in every notebook to ensure readability and maintainability.
-
-1. **Title & Purpose**: H1 Title and a brief description of the experiment goals.
-2. **Imports**: Group standard libraries, third-party, and usage-specific imports.
-3. **Configs**: Define **Global Constants** (paths, random seeds, hyperparameters) here. No magic numbers deeper in the code.
-4. **Datasets**: Load, validate, and split data.
-5. **Analysis (EDA)**: Inspect target distributions and correlations.
-6. **Modeling**: Define and train `sklearn.pipeline.Pipeline` objects.
-7. **Evaluations**: Compute metrics and visualize performance on held-out data.
-
-### 2. Configuration Standards
-
-Expose all "knobs" at the top of the notebook for easy experimentation.
-
-- **Randomness**: Define `RANDOM_STATE = 42` and use it in splits and model initialization.
-- **Paths**: Use `pathlib` for robust path handling.
-
-  ```python
-  from pathlib import Path
-  ROOT = Path("..")
-  DATA_PATH = ROOT / "data" / "input.parquet"
-  ```
-
-- **Hyperparameters**: Group model params (e.g., `N_ESTIMATORS`, `MAX_DEPTH`).
-- **Toggles**: Use booleans for expensive operations (e.g., `USE_GPU = True`, `RUN_GRID_SEARCH = False`).
-
-### 3. Data Management
-
-Ensure data integrity and prevent leakage.
-
-- **Loading**: Prefer `pd.read_parquet` for speed/types, or `pd.read_csv`.
-- **Splitting**:
-  - **Always** split into `X_train`, `X_test`, `y_train`, `y_test` **before** any data-dependent transformations (imputation, scaling).
-  - **Random Split**: Use `sklearn.model_selection.train_test_split` with `stratify` for balanced classification.
-  - **Time Series**: Use `sklearn.model_selection.TimeSeriesSplit` if data has a temporal dimension (do NOT shuffle).
-  - Use `random_state=RANDOM_STATE`.
-
-### 4. Pipeline Construction
-
-Prohibit raw data transformations on the full dataset.
-
-- **Mandate**: Use `sklearn.pipeline.Pipeline` or `ColumnTransformer`.
-- **Why**: Automation of `fit` on train and `transform` on test prevents data leakage.
-- **Example**:
-
-  ```python
-  from sklearn.pipeline import Pipeline
-  from sklearn.preprocessing import StandardScaler, OneHotEncoder
-  from sklearn.impute import SimpleImputer
-  from sklearn.compose import ColumnTransformer
-
-  CACHE = "./.cache" # Define a cache directory
-
-  numeric_transformer = Pipeline(steps=[
-      ('imputer', SimpleImputer(strategy='median')),
-      ('scaler', StandardScaler())
-  ])
-
-  preprocessor = ColumnTransformer(transformers=[
-      ('num', numeric_transformer, numeric_features)
-  ])
-
-  # Use 'memory' to cache transformer outputs, speeding up GridSearch
-  model = Pipeline(steps=[
-      ('preprocessor', preprocessor),
-      ('classifier', RandomForestClassifier())
-  ], memory=CACHE)
-  ```
-
-### 5. Evaluation & Visualization
-
-Go beyond accuracy/MSE.
-
-- **Metrics**: Use `sklearn.metrics` appropriate for the task (F1, ROC-AUC, RMSE, MAE).
-- **Baselines**: Compare against a "Dummy" model (mean/mode) to verify learning.
-- **Visualization**:
-  - **Regression**: Residual plots, Actual vs Predicted.
-  - **Classification**: Confusion Matrix, ROC Curve, Precision-Recall.
-  - **Feature Importance**: Visualize `feature_importances_` or SHAP values.
-
-### 6. Transition to Production
-
-Facilitate the move from notebook to python package (`src/`).
-
-- **Function Refactoring**: Once a block of code is stable (e.g., a complex data cleaning step), refactor it into a function _within_ the notebook. This makes moving it to a `.py` file trivial later.
-- **Cell Tagging**: Use tags like `parameters` (for Papermill) or `export` to mark cells that should be part of the final documentation or automated pipeline.
-- **Clean State**: Ensure the notebook runs top-to-bottom (`Restart Kernel and Run All`) without errors before committing.
-
-## Self-Correction Checklist
-
-- [ ] **No Magic Numbers**: Are all parameters in the `Configs` section?
-- [ ] **No Data Leakage**: Is `fit` called ONLY on `X_train`?
-- [ ] **Reproducibility**: Is `random_state` set for all stochastic operations?
-- [ ] **Resilience**: are paths defined relative to the project root?
-- [ ] **Clarity**: Does the notebook read like a report (Markdown cells explaining the _Why_)?
diff --git a/.gemini/skills/MLOps Validation/SKILL.md b/.gemini/skills/MLOps Validation/SKILL.md
deleted file mode 100644
index 8d07123..0000000
--- a/.gemini/skills/MLOps Validation/SKILL.md	
+++ /dev/null
@@ -1,82 +0,0 @@
----
-name: MLOps Validation
-description: Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
----
-
-# MLOps Validation
-
-## Goal
-
-To ensure software quality, reliability, and security through automated validation layers. This skill enforces **Strict Typing** (`ty`), **Unified Linting** (`ruff`), **Comprehensive Testing** (`pytest`), and **Structured Logging** (`loguru`).
-
-## Prerequisites
-
-- **Language**: Python
-- **Manager**: `uv`
-- **Context**: Ensuring code quality before merge/deploy.
-
-## Instructions
-
-### 1. Static Analysis (Typing & Linting)
-
-Catch errors before they run.
-
-1. **Typing**:
-   - **Tool**: `ty`.
-   - **Rule**: No `Any` (unless absolutely necessary). Fully typed function signatures.
-   - **DataFrames**: Use `pandera` schemas to validate DataFrame structures/types.
-   - **Classes**: Use `pydantic` for data modeling and runtime validation.
-2. **Linting & Formatting**:
-   - **Tool**: `ruff` (replaces black, isort, pylint, flake8).
-   - **Rule**: Zero tolerance for linter errors. Use `noqa` sparingly and with justification.
-   - **Config**: Centralize in `pyproject.toml`.
-
-### 2. Testing Strategy
-
-Verify behavior and prevent regressions.
-
-1. **Tool**: `pytest`.
-2. **Structure**: Mirror `src/` in `tests/`.
-
-   ```text
-   src/pkg/mod.py -> tests/test_mod.py
-   ```
-
-3. **Fixtures**: Use `tests/conftest.py` for shared setup (mock data, temp paths).
-4. **Coverage**: Aim for high coverage (>80%) on core business logic. Use `pytest-cov`.
-5. **Pattern**: Use **Given-When-Then** in comments.
-
-   ```python
-   def test_pipeline_execution(input_data):
-       # Given: Valid input data
-       # When: The pipeline processes the data
-       # Then: The output content matches expectations
-   ```
-
-### 3. Structured Logging
-
-Enable observability and debugging.
-
-1. **Tool**: `loguru` (replacing stdlib `logging`).
-2. **Format**: Use structured logging (JSON) in production for queryability.
-3. **Levels**:
-   - `DEBUG`: Low-level tracing (payloads, internal state).
-   - `INFO`: Key business events (Job started, Model saved).
-   - `ERROR`: Actionable failures (with stack traces).
-4. **Context**: Include context (Job ID, Model Version) in logs.
-
-### 4. Security
-
-Protect the supply chain and runtime.
-
-1. **Dependencies**: Use `GitHub Dependabot` to patch vulnerable packages.
-2. **Code Scanning**: Run `bandit` to detect hardcoded secrets or unsafe patterns (e.g., `eval`, `yaml.load`).
-3. **Secrets**: **NEVER** log secrets. Sanitize outputs.
-
-## Self-Correction Checklist
-
-- [ ] **Type Safety**: Does `ty` pass without errors?
-- [ ] **Lint Cleanliness**: Does `ruff check` pass?
-- [ ] **Test Discovery**: Does `pytest` successfully find modules in `src/`?
-- [ ] **Log Format**: Are production logs serializing to JSON?
-- [ ] **Security**: Has `bandit` scanned the codebase?
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index fda4d98..8cfea7c 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -1,15 +1,38 @@
 # https://docs.github.com/en/code-security/dependabot/working-with-dependabot/dependabot-options-reference
+# Minor and patch updates arrive grouped, one pull request per ecosystem; majors stay
+# separate so a breaking bump is always reviewed on its own.
 version: 2
 updates:
   - package-ecosystem: "uv"
     directory: "/"
     schedule:
       interval: "weekly"
+      day: "monday"
+    commit-message:
+      prefix: "chore(deps)"
+    groups:
+      python:
+        patterns: ["*"]
+        update-types: ["minor", "patch"]
   - package-ecosystem: "github-actions"
     directory: "/"
     schedule:
       interval: "weekly"
+      day: "monday"
+    commit-message:
+      prefix: "chore(deps)"
+    groups:
+      actions:
+        patterns: ["*"]
+        update-types: ["minor", "patch"]
   - package-ecosystem: "docker"
     directory: "/"
     schedule:
       interval: "weekly"
+      day: "monday"
+    commit-message:
+      prefix: "chore(deps)"
+    groups:
+      docker:
+        patterns: ["*"]
+        update-types: ["minor", "patch"]
diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml
index e9df874..4db52cc 100644
--- a/.github/workflows/cd.yml
+++ b/.github/workflows/cd.yml
@@ -6,8 +6,10 @@ permissions:
   contents: read
 jobs:
   pages:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
+    timeout-minutes: 20
     permissions:
+      contents: read
       pages: write
       id-token: write
     environment:
@@ -16,8 +18,12 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v7
+        with:
+          persist-credentials: false # no step pushes back to the repository
       - name: Install mise toolchain
         uses: jdx/mise-action@v4
+        with:
+          cache: false # a poisoned tool cache must never reach published artifacts
       - name: Build API documentation
         run: mise run docs
       - name: Configure Pages
@@ -30,14 +36,22 @@ jobs:
         id: deployment
         uses: actions/deploy-pages@v5
   image:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
+    timeout-minutes: 30
     permissions:
+      contents: read
       packages: write
     steps:
       - name: Checkout repository
         uses: actions/checkout@v7
+        with:
+          persist-credentials: false # the registry login below carries its own token
       - name: Set lower-case image path
-        run: echo "IMAGE=$(echo "ghcr.io/${{ github.repository }}" | tr '[:upper:]' '[:lower:]')" >> "$GITHUB_ENV"
+        # GitHub expands `${{ }}` before the shell runs, so repository references reach
+        # the script through `env:` rather than being interpolated into the command.
+        env:
+          REPOSITORY: ${{ github.repository }}
+        run: echo "IMAGE=$(echo "ghcr.io/$REPOSITORY" | tr '[:upper:]' '[:lower:]')" >> "$GITHUB_ENV"
       - name: Log in to GitHub Container Registry
         uses: docker/login-action@v4
         with:
@@ -51,8 +65,6 @@ jobs:
         with:
           context: .
           push: true
-          cache-to: type=gha
-          cache-from: type=gha
           tags: |
             ${{ env.IMAGE }}:latest
             ${{ env.IMAGE }}:${{ github.ref_name }}
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4d9542c..cd490f1 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -10,17 +10,18 @@ concurrency:
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 jobs:
   checks: # name kept as "checks" to satisfy the repository's required-status-check ruleset
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
+    timeout-minutes: 20
     steps:
       - name: Checkout repository
         uses: actions/checkout@v7
+        with:
+          persist-credentials: false # no step pushes back to the repository
       - name: Install mise toolchain
         uses: jdx/mise-action@v4
-      - name: Format sources
-        run: mise run format
-      - name: Run checks
-        run: mise run check
-      - name: Run tests
-        run: mise run test
+        with:
+          cache: true
+      - name: Run canonical gate
+        run: mise run all
       - name: Verify no changes
-        run: git diff --exit-code
+        run: test -z "$(git status --porcelain)"
diff --git a/.github/workflows/security.yml b/.github/workflows/security.yml
new file mode 100644
index 0000000..9f8ce23
--- /dev/null
+++ b/.github/workflows/security.yml
@@ -0,0 +1,30 @@
+name: Security
+# Push CI scans only the latest commits, so a secret committed earlier and later
+# removed would never be seen again. This is the full-history counterpart: same
+# pinned scanners, whole history, no reports to parse — a finding fails the job.
+on:
+  schedule:
+    - cron: "17 3 * * 1"
+  workflow_dispatch:
+permissions:
+  contents: read
+concurrency:
+  group: security
+  cancel-in-progress: true
+jobs:
+  scan:
+    name: Full-history scan
+    runs-on: ubuntu-latest
+    timeout-minutes: 30
+    steps:
+      - name: Checkout complete history
+        uses: actions/checkout@v7
+        with:
+          fetch-depth: 0
+          persist-credentials: false # scanners only read the checkout
+      - name: Install mise system
+        uses: jdx/mise-action@v4
+      - name: Scan complete Git history
+        run: gitleaks git --redact=100 --verbose
+      - name: Scan full checkout
+        run: trivy fs .
diff --git a/.github/zizmor.yml b/.github/zizmor.yml
new file mode 100644
index 0000000..a2a4b9b
--- /dev/null
+++ b/.github/zizmor.yml
@@ -0,0 +1,10 @@
+# Copy to .github/zizmor.yml.
+# Actions are deliberately pinned to major-version tags (see the upgrade-tools
+# skill): tags track security patches within a major, at the cost of trusting
+# the tag. zizmor's default policy demands hash-pins; relax it to ref-pins so
+# the audit enforces the actual policy instead of fighting it.
+rules:
+  unpinned-uses:
+    config:
+      policies:
+        "*": ref-pin
diff --git a/.gitignore b/.gitignore
index 26750c0..e36926d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,6 +23,7 @@
 
 # Project
 /docs/*
+/mlflow.db
 /mlruns/*
 /mlartifacts/*
 /outputs/*
diff --git a/AGENTS.md b/AGENTS.md
index d260161..b80f2ff 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -8,15 +8,17 @@ Context and rules for AI agents working in this repository. Humans should start
 - **Description**: reference implementation for the [MLOps Coding Course](https://mlops-coding-course.fmind.dev), generated from [cookiecutter-mlops-package](https://github.com/fmind/cookiecutter-mlops-package).
 - **Language**: Python 3.14+ (`pyproject.toml`), managed with `uv`.
 - **Stack**: MLflow (tracking, registry, projects, evaluation), scikit-learn, pandas, Pydantic + Pandera validation, OmegaConf YAML configs, loguru.
+- **Skills**: the reusable practices behind this package are published as Agent Skills in [mlops-coding-skills](https://github.com/MLOps-Courses/mlops-coding-skills). Install them from there rather than vendoring a copy here — a copy drifts, and this repository already lost a month to proving it.
 
 ## Setup & core commands
 
 All work goes through `mise` (see `mise.toml`); git hooks (`lefthook.yml`) and CI call the same tasks.
 
+- Everything: `mise run all` — format, check, test, build. This is the gate; CI runs this exact task and nothing else.
 - Install: `mise run install` — sync the virtualenv (`uv sync`) and install git hooks.
-- Format: `mise run format` — `ruff` (import sort + format) and `dprint` (JSON/Markdown/TOML/YAML).
-- Check: `mise run check` — `ruff` lint, `ty` types, `pip-audit` deps, `dprint`/`validate-pyproject`/`uv lock` format, `gitleaks` secrets, `trivy` config.
-- Test: `mise run test` — `pytest` with coverage (fails under 80%).
+- Format: `mise run format` — `ruff` (import sort + format, including Python inside Markdown) and `dprint` (JSON/Markdown/TOML/YAML).
+- Check: `mise run check` — `ruff` lint, `ty` types, `pip-audit` deps, `dprint`/`validate-pyproject`/`uv lock` format, `gitleaks` secrets, `trivy` filesystem scan, `hadolint` Dockerfile, `actionlint` + `zizmor` workflows.
+- Test: `mise run test` — `pytest` with coverage (fails under 100%); `mise run test:parallel` is a faster, coverage-free local loop.
 - Build: `mise run build` — `uv build` (wheel + sdist); `mise run build:image` builds the Docker image.
 - Docs: `mise run docs` — `pdoc` API reference into `docs/`.
 - MLflow jobs: `mise run project` runs every job; `mise run project:run <name>` runs one (`confs/<name>.yaml`).
@@ -31,12 +33,13 @@ A change is complete only when, locally, `mise run format` is clean, `mise run c
 - **Config over hardcoding**: jobs and objects are Pydantic models parsed from OmegaConf YAML in `confs/`; validate and fail fast. Dataframes are validated at boundaries with Pandera schemas (`core/schemas.py`).
 - **Typing**: modern annotations (`list[str]`, `X | Y`); keep `ty check` clean. `import typing as T` is the project convention.
 - **Logging**: `loguru` via `LoggerService`; no bare prints in library code.
-- **MLflow**: the file store (`./mlruns`) is opted in via `MLFLOW_ALLOW_FILE_STORE` for local development; prefer a database backend in production. Models are logged with `name=` and referenced by `models:/name@alias` or `models:/name/version`.
+- **MLflow**: tracking and registry run on a SQLite backend (`sqlite:///mlflow.db`); artifact files stay on disk under `./mlruns`. This is the same SQLAlchemy store shape as a production Postgres and the store the model registry is designed for, so moving up is a `MLFLOW_TRACKING_URI` change, not a rewrite. Models are logged with `name=` and referenced by `models:/name@alias` or `models:/name/version`.
 - **Commits**: Conventional Commits (`feat:`, `fix:`, `refactor:`, `chore:`); no attribution in commit messages. Releases use `git-cliff` (see the release process).
 
 ## Repository layout
 
 - `src/bikes/` — package: `core/` (metrics, models, schemas), `io/` (configs, datasets, registries, services), `jobs/` (tuning, training, promotion, inference, evaluations, explanations), `utils/` (searchers, signers, splitters), plus `settings.py`, `scripts.py`, `__main__.py`.
 - `confs/` — one OmegaConf YAML per MLflow job; `tests/` — `pytest` suite mirroring `src/` with fixtures in `conftest.py`.
-- `pyproject.toml` — dependencies and `ruff`/`ty`/`pytest` config; `mise.toml` — tasks and pinned tools; `lefthook.yml` — git hooks; `dprint.jsonc`/`trivy.yaml`/`cliff.toml` — formatter, scanner, changelog config.
-- `Dockerfile`/`docker-compose.yml`/`MLproject`/`python_env.yaml` — container image, local MLflow server, and MLflow Projects reproducible runs.
+- `pyproject.toml` — dependencies and `ruff`/`ty`/`pytest` config; `mise.toml`/`mise.lock` — tasks and pinned, locked tools; `lefthook.yml` — git hooks; `dprint.jsonc`/`trivy.yaml`/`cliff.toml` — formatter, scanner, changelog config.
+- `.github/` — `workflows/` (`ci.yml` runs `mise run all`, `cd.yml` publishes docs and the image, `security.yml` rescans the full history weekly), `dependabot.yml`, `zizmor.yml`, `rulesets/main.json`.
+- `Dockerfile`/`docker-compose.yml`/`MLproject` — container image, local MLflow server, and MLflow Projects reproducible runs (`--env-manager=local` reuses the uv environment, so there is no `python_env.yaml`).
diff --git a/Dockerfile b/Dockerfile
index 6da6b1b..df48cd7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -5,7 +5,8 @@ FROM python:3.14-slim AS build
 ENV UV_COMPILE_BYTECODE=1
 ENV UV_LINK_MODE=copy
 WORKDIR /app
-COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
+# Pinned, not `:latest` — a floating tag is invisible to Dependabot's docker ecosystem.
+COPY --from=ghcr.io/astral-sh/uv:0.12.3 /uv /uvx /bin/
 RUN --mount=type=cache,target=/root/.cache/uv \
   --mount=type=bind,source=uv.lock,target=uv.lock \
   --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
@@ -17,9 +18,11 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 FROM python:3.14-slim
 ENV PYTHONUNBUFFERED=1
 WORKDIR /app
-RUN groupadd -r app && useradd -r -g app -m app
-USER app
-COPY --from=build --chown=app:app /app/.venv /app/.venv
+# Fixed numeric uid/gid: stable file ownership across rebuilds and bind mounts, and
+# resolvable by a host that does not share this image's /etc/passwd.
+RUN groupadd -r -g 10001 app && useradd -r -u 10001 -g app -m app
+USER 10001:10001
+COPY --from=build --chown=10001:10001 /app/.venv /app/.venv
 ENV PATH="/app/.venv/bin:$PATH"
 ENTRYPOINT ["bikes"]
 CMD ["--help"]
diff --git a/LICENSE.txt b/LICENSE.txt
index 0e77b64..63acf86 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,3 +1,7 @@
+MIT License
+
+Copyright (c) 2026 Médéric Hurier (Fmind)
+
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
diff --git a/README.md b/README.md
index 8e3015b..9bef2a4 100644
--- a/README.md
+++ b/README.md
@@ -39,6 +39,7 @@ You can use this package as part of your MLOps toolkit or platform (e.g., Model
     - [Tasks: Mise](#tasks-mise)
   - [CI/CD](#cicd)
     - [Runner: GitHub Actions](#runner-github-actions)
+    - [Workflow Linting: actionlint + zizmor](#workflow-linting-actionlint--zizmor)
   - [CLI](#cli)
     - [Parser: Argparse](#parser-argparse)
     - [Logging: Loguru](#logging-loguru)
@@ -75,6 +76,7 @@ You can use this package as part of your MLOps toolkit or platform (e.g., Model
     - [Format: Wheel](#format-wheel)
     - [Manager: uv](#manager-uv)
     - [Runtime: Docker](#runtime-docker)
+    - [Image Linting: hadolint](#image-linting-hadolint)
   - [Programming](#programming)
     - [Language: Python](#language-python)
     - [Version: Uv](#version-uv)
@@ -123,8 +125,8 @@ This section details the requirements, actions, and next steps to kickstart your
 ## Prerequisites
 
 - [Python>=3.14](https://www.python.org/downloads/): to benefit from [the latest features and performance improvements](https://docs.python.org/3/whatsnew/3.14.html)
-- [uv>=0.9.0](https://docs.astral.sh/uv/): to initialize the project [virtual environment](https://docs.python.org/3/library/venv.html) and its dependencies
-- [mise](https://mise.jdx.dev/): to run the canonical project tasks (`install`, `format`, `check`, `test`, `build`) shared by git hooks and CI
+- [uv>=0.12](https://docs.astral.sh/uv/): to initialize the project [virtual environment](https://docs.python.org/3/library/venv.html) and its dependencies
+- [mise](https://mise.jdx.dev/): to run the canonical project tasks (`install`, `format`, `check`, `test`, `build`, and `all`) shared by git hooks and CI
 
 ## Installation
 
@@ -357,11 +359,23 @@ Execution of automated workflows on code push and releases.
   - Native on GitHub
   - Simple workflow syntax
   - Lots of configs if needed
+  - Runs one command, `mise run all`, so CI and local hooks cannot disagree
 - **Limitations**:
   - SaaS Service
 - **Alternatives**:
   - [GitLab](https://about.gitlab.com/): can be installed on-premise
 
+### Workflow Linting: [actionlint](https://github.com/rhysd/actionlint) + [zizmor](https://docs.zizmor.sh/)
+
+- **Motivations**:
+  - Workflows are code: `actionlint` checks their schema, expressions, and embedded shell
+  - `zizmor` audits them for security (template injection, credential persistence, cache poisoning)
+  - Both run offline inside `mise run check:actions`, so a broken workflow fails before it is pushed
+- **Limitations**:
+  - `zizmor` defaults to demanding SHA-pinned actions; `.github/zizmor.yml` relaxes that to the tag-pinning policy this project actually follows
+- **Alternatives**:
+  - Discovering the mistake from a red run on `main`
+
 ## CLI
 
 Integrations with the Command-Line Interface (CLI) of your system.
@@ -681,7 +695,7 @@ Toolkit to handle machine learning models.
   - [Neptune.ai](https://neptune.ai/): SaaS solution
   - [Weights and Biases](https://wandb.ai/site): SaaS solution
 
-> **MLflow 3**: MLflow 3 deprecated the local filesystem store, so for local development this package opts in via the `MLFLOW_ALLOW_FILE_STORE` environment variable (see `.env`) and serves the `./mlruns` store with `mise run mlflow:serve`. Prefer a database backend (e.g., PostgreSQL) in production.
+> **MLflow 3**: MLflow 3 put the local filesystem store in maintenance mode, so this package tracks runs and registered models in a SQLite database (`sqlite:///mlflow.db`); artifact files still live on disk under `./mlruns`. Browse it with `mise run mlflow:serve`. SQLite is a real SQLAlchemy backend — the same store shape as a production PostgreSQL, the one the model registry is actually designed for, and a one-line change (`MLFLOW_TRACKING_URI`) away from it.
 
 ## Package
 
@@ -708,7 +722,7 @@ Define and build modern Python package.
   - Doesn't ship with C/C++ dependencies (e.g., CUDA)
     - i.e., use Docker containers for this case
 - **Alternatives**:
-  - [Source](https://docs.python.org/3/distutils/sourcedist.html): older format, less powerful
+  - [Source](https://packaging.python.org/en/latest/specifications/source-distribution-format/): older format, less powerful
   - [Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html): slow and hard to manage
 
 ### Manager: [uv](https://docs.astral.sh/uv/)
@@ -736,6 +750,16 @@ Define and build modern Python package.
 - **Alternatives**:
   - [Conda](https://docs.conda.io/en/latest/): slow and heavy resolver
 
+### Image Linting: [hadolint](https://github.com/hadolint/hadolint)
+
+- **Motivations**:
+  - Catches Dockerfile mistakes (unpinned bases, root users, unresolvable uids) before a build
+  - Runs offline in under a second inside `mise run check:dockerfile`
+- **Limitations**:
+  - Lints the recipe, not the built image; `trivy` covers the image contents
+- **Alternatives**:
+  - Reading the [Dockerfile best practices](https://docs.docker.com/build/building/best-practices/) page every time
+
 ## Programming
 
 Select your programming environment.
@@ -817,7 +841,7 @@ Select your programming environment.
   - Not as feature-rich as alternative solutions.
 - **Alternatives**:
   - [Databricks Lineage](https://docs.databricks.com/en/admin/system-tables/lineage.html): limited to Databricks.
-  - [OpenLineage and Marquez](https://marquezproject.github.io/): open-source and flexible.
+  - [OpenLineage and Marquez](https://marquezproject.ai/): open-source and flexible.
 
 ### Explainability: [SHAP](https://shap.readthedocs.io/en/latest/)
 
@@ -931,7 +955,7 @@ In practice, this mean you can implement software contracts with interface and s
 
 For instance, you can implement several jobs in `src/[package]/jobs/*.py` and swap them in your configuration.
 
-To learn more about the mechanism select for this package, you can check the documentation for [Pydantic Tagged Unions](https://docs.pydantic.dev/dev-v2/usage/types/unions/#discriminated-unions-aka-tagged-unions).
+To learn more about the mechanism select for this package, you can check the documentation for [Pydantic Tagged Unions](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions).
 
 ### [IO Separation](https://en.wikibooks.org/wiki/Haskell/Understanding_monads/IO)
 
@@ -992,6 +1016,7 @@ Python provides the [typing module](https://docs.python.org/3/library/typing.htm
 def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "Model":
     """Fit the model on the given inputs and target."""
 
+
 @abc.abstractmethod
 def predict(self, inputs: schemas.Inputs) -> schemas.Outputs:
     """Generate an output with the model for the given inputs."""
@@ -1058,7 +1083,6 @@ Polymorphism combined with SOLID Principles allows to easily swap your code comp
 
 ```python
 class Reader(abc.ABC, pdt.BaseModel):
-
     @abc.abstractmethod
     def read(self) -> pd.DataFrame:
         """Read a dataframe from a dataset."""
diff --git a/docker-compose.yml b/docker-compose.yml
index e651086..9d3a304 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -2,10 +2,13 @@
 
 services:
   mlflow:
-    image: ghcr.io/mlflow/mlflow:v3.14.0
+    image: ghcr.io/mlflow/mlflow:v3.15.1
     ports:
       - 5000:5000
-    environment:
-      # MLflow 3 puts the filesystem store in maintenance mode; opt in for local development.
-      - MLFLOW_ALLOW_FILE_STORE=true
-    command: mlflow server --host 0.0.0.0 --port 5000
+    volumes:
+      - ./mlflow.db:/mlflow/mlflow.db
+      - ./mlruns:/mlflow/mlruns
+    working_dir: /mlflow
+    # SQLAlchemy backend, same store the package writes to locally: the file store is in
+    # maintenance mode in MLflow 3 and was never designed for the model registry.
+    command: mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri sqlite:///mlflow.db --artifacts-destination ./mlruns
diff --git a/dprint.jsonc b/dprint.jsonc
index 92d71b8..8360fd0 100644
--- a/dprint.jsonc
+++ b/dprint.jsonc
@@ -13,7 +13,7 @@
   },
   "plugins": [
     "https://plugins.dprint.dev/g-plane/pretty_yaml-v0.6.0.wasm",
-    "https://plugins.dprint.dev/json-0.22.0.wasm",
+    "https://plugins.dprint.dev/json-0.23.0.wasm",
     "https://plugins.dprint.dev/markdown-0.22.1.wasm",
     "https://plugins.dprint.dev/toml-0.7.0.wasm"
   ]
diff --git a/lefthook.yml b/lefthook.yml
index 5da6a33..bc95885 100644
--- a/lefthook.yml
+++ b/lefthook.yml
@@ -1,24 +1,26 @@
 # https://lefthook.dev
 # Thin hooks: every command delegates to a `mise run` task so hooks and CI stay identical.
+# Lefthook orders commands alphabetically, so priorities are explicit: formatters (10)
+# restage before the staged secret scan (20) and the whole-tree checks (30) read from disk.
 
 pre-commit:
   parallel: false
   commands:
     format:dprint:
-      priority: 1
+      priority: 10
       glob: "*.{json,md,toml,yaml,yml}"
       run: mise run format:dprint {staged_files}
       stage_fixed: true
     format:python:
-      priority: 1
+      priority: 10
       glob: "*.py"
       run: mise run format:python {staged_files}
       stage_fixed: true
     check:leaks:
-      priority: 2
+      priority: 20
       run: mise run check:leaks --staged
     check:
-      priority: 3
+      priority: 30
       run: mise run check
 
 pre-push:
diff --git a/mise.lock b/mise.lock
new file mode 100644
index 0000000..f50223d
--- /dev/null
+++ b/mise.lock
@@ -0,0 +1,103 @@
+# @generated - this file is auto-generated by `mise lock` https://mise.jdx.dev/dev-tools/mise-lock.html
+
+[[tools.actionlint]]
+version = "1.7.12"
+backend = "aqua:rhysd/actionlint"
+
+[tools.actionlint."platforms.linux-x64"]
+checksum = "sha256:8aca8db96f1b94770f1b0d72b6dddcb1ebb8123cb3712530b08cc387b349a3d8"
+url = "https://github.com/rhysd/actionlint/releases/download/v1.7.12/actionlint_1.7.12_linux_amd64.tar.gz"
+url_api = "https://api.github.com/repos/rhysd/actionlint/releases/assets/384924896"
+
+[tools.actionlint."platforms.macos-arm64"]
+checksum = "sha256:aba9ced2dee8d27fecca3dc7feb1a7f9a52caefa1eb46f3271ea66b6e0e6953f"
+url = "https://github.com/rhysd/actionlint/releases/download/v1.7.12/actionlint_1.7.12_darwin_arm64.tar.gz"
+url_api = "https://api.github.com/repos/rhysd/actionlint/releases/assets/384924893"
+
+[[tools.dprint]]
+version = "0.55.2"
+backend = "aqua:dprint/dprint"
+
+[tools.dprint."platforms.linux-x64"]
+checksum = "sha256:45fc0eef3216af21c4c22c6e7e233aa45c3080fac07b6e94db7008a5c8e5c67a"
+url = "https://github.com/dprint/dprint/releases/download/0.55.2/dprint-x86_64-unknown-linux-musl.zip"
+url_api = "https://api.github.com/repos/dprint/dprint/releases/assets/477041351"
+
+[tools.dprint."platforms.macos-arm64"]
+checksum = "sha256:e9ba8ed7988f3350501a8cf8af92da616cdec8d9d5c831c069293c587311b49d"
+url = "https://github.com/dprint/dprint/releases/download/0.55.2/dprint-aarch64-apple-darwin.zip"
+url_api = "https://api.github.com/repos/dprint/dprint/releases/assets/477041350"
+
+[[tools.git-cliff]]
+version = "2.13.1"
+backend = "aqua:orhun/git-cliff"
+
+[tools.git-cliff."platforms.linux-x64"]
+checksum = "sha256:200d2535da6d9703f3bcc8a4d159c3b55eacdb01cf2148c55b3eee9dd04d5249"
+url = "https://github.com/orhun/git-cliff/releases/download/v2.13.1/git-cliff-2.13.1-x86_64-unknown-linux-musl.tar.gz"
+url_api = "https://api.github.com/repos/orhun/git-cliff/releases/assets/405850671"
+
+[tools.git-cliff."platforms.macos-arm64"]
+checksum = "sha256:21547ae4a0421164070ab75c2522864ea5565858a011fabc5f583061b20f1226"
+url = "https://github.com/orhun/git-cliff/releases/download/v2.13.1/git-cliff-2.13.1-aarch64-apple-darwin.tar.gz"
+url_api = "https://api.github.com/repos/orhun/git-cliff/releases/assets/405849885"
+
+[[tools.gitleaks]]
+version = "8.30.1"
+backend = "aqua:gitleaks/gitleaks"
+
+[tools.gitleaks."platforms.linux-x64"]
+checksum = "sha256:551f6fc83ea457d62a0d98237cbad105af8d557003051f41f3e7ca7b3f2470eb"
+url = "https://github.com/gitleaks/gitleaks/releases/download/v8.30.1/gitleaks_8.30.1_linux_x64.tar.gz"
+url_api = "https://api.github.com/repos/gitleaks/gitleaks/releases/assets/378332058"
+
+[tools.gitleaks."platforms.macos-arm64"]
+checksum = "sha256:b40ab0ae55c505963e365f271a8d3846efbc170aa17f2607f13df610a9aeb6a5"
+url = "https://github.com/gitleaks/gitleaks/releases/download/v8.30.1/gitleaks_8.30.1_darwin_arm64.tar.gz"
+url_api = "https://api.github.com/repos/gitleaks/gitleaks/releases/assets/378332059"
+
+[[tools.hadolint]]
+version = "2.15.1"
+backend = "aqua:hadolint/hadolint"
+
+[tools.hadolint."platforms.linux-x64"]
+checksum = "sha256:c7187db94eeeeca956519a6af171adc31453941a1e777961f6e680f697c8c507"
+url = "https://github.com/hadolint/hadolint/releases/download/v2.15.1/hadolint-linux-x86_64"
+url_api = "https://api.github.com/repos/hadolint/hadolint/releases/assets/496546506"
+
+[tools.hadolint."platforms.macos-arm64"]
+checksum = "sha256:5c09f3213f8e40406abe048233d985eebef336d4a6a20021be47fadb6cf480a2"
+url = "https://github.com/hadolint/hadolint/releases/download/v2.15.1/hadolint-macos-arm64"
+url_api = "https://api.github.com/repos/hadolint/hadolint/releases/assets/496546505"
+
+[[tools.trivy]]
+version = "0.73.0"
+backend = "aqua:aquasecurity/trivy"
+
+[tools.trivy."platforms.linux-x64"]
+checksum = "sha256:2edd39da482bb4e9831962487b68f68e3928ec3137794757f54d00383d79547b"
+url = "https://github.com/aquasecurity/trivy/releases/download/v0.73.0/trivy_0.73.0_Linux-64bit.tar.gz"
+url_api = "https://api.github.com/repos/aquasecurity/trivy/releases/assets/499939909"
+
+[tools.trivy."platforms.macos-arm64"]
+checksum = "sha256:80cc25faaf6378e37701202d0b4f9f43d9e413d198d594ba60fdf559fe44a683"
+url = "https://github.com/aquasecurity/trivy/releases/download/v0.73.0/trivy_0.73.0_macOS-ARM64.tar.gz"
+url_api = "https://api.github.com/repos/aquasecurity/trivy/releases/assets/499939985"
+
+[[tools.uv]]
+version = "0.12.3"
+backend = "asdf:uv"
+
+[[tools.zizmor]]
+version = "1.29.0"
+backend = "aqua:zizmorcore/zizmor"
+
+[tools.zizmor."platforms.linux-x64"]
+checksum = "sha256:dd96df044a6e8538d5f423790f453bdd03d49e5b2bcc38214acc41a2f1297839"
+url = "https://github.com/zizmorcore/zizmor/releases/download/v1.29.0/zizmor-x86_64-unknown-linux-gnu.tar.gz"
+url_api = "https://api.github.com/repos/zizmorcore/zizmor/releases/assets/498263145"
+
+[tools.zizmor."platforms.macos-arm64"]
+checksum = "sha256:720322fade9e83a9c7953944c438f2ba942636b86b96a8f0e6b15ce94c8a6b6f"
+url = "https://github.com/zizmorcore/zizmor/releases/download/v1.29.0/zizmor-aarch64-apple-darwin.tar.gz"
+url_api = "https://api.github.com/repos/zizmorcore/zizmor/releases/assets/498263141"
diff --git a/mise.toml b/mise.toml
index 7d77f2c..00f8c76 100644
--- a/mise.toml
+++ b/mise.toml
@@ -3,18 +3,26 @@
 
 [env]
 _.source = ".env"
-# MLflow 3 refuses the filesystem store by default; opt in for local development so the
-# `mlflow` CLI (project runner, server) works. Prefer a database backend in production.
-MLFLOW_ALLOW_FILE_STORE = "true"
 
 [settings.task]
 run_auto_install = false
 
 [tools]
-uv = "latest"
+actionlint = "latest"
 dprint = "latest"
+git-cliff = "latest"
 gitleaks = "latest"
+hadolint = "latest"
 trivy = "latest"
+uv = "latest"
+zizmor = "latest"
+
+# ALL
+
+[tasks.all]
+alias = "a"
+description = "Format, check, test, and build the project (the canonical gate)"
+run = ["mise run format", "mise run check", "mise run test", "mise run build"]
 
 # INSTALL
 
@@ -33,12 +41,18 @@ depends = ["install:python"]
 run = "uv run lefthook install"
 
 [tasks."install:rulesets"]
-description = "Install GitHub branch rulesets"
+description = "Install or update the GitHub branch ruleset (idempotent)"
+# POST creates a duplicate every time it runs, so look the ruleset up by name first and
+# PUT over the existing one. Re-running this task must be a no-op, not a second ruleset.
 run = """
-repo=$(gh repo view --json=name --jq=.name)
-owner=$(gh repo view --json=owner --jq=.owner.login)
-gh api --method POST -H "Accept: application/vnd.github+json" \
-  "/repos/$owner/$repo/rulesets" --input=.github/rulesets/main.json
+repo=$(gh repo view --json=nameWithOwner --jq=.nameWithOwner)
+name=$(jq -r .name .github/rulesets/main.json)
+id=$(gh api "/repos/$repo/rulesets" --jq ".[] | select(.name == \\"$name\\") | .id" | head -n1)
+if [ -n "$id" ]; then
+  gh api --method PUT "/repos/$repo/rulesets/$id" --input=.github/rulesets/main.json
+else
+  gh api --method POST "/repos/$repo/rulesets" --input=.github/rulesets/main.json
+fi
 """
 
 # FORMAT
@@ -62,14 +76,24 @@ run = "dprint fmt"
 alias = "c"
 description = "Run all static checks in parallel"
 depends = [
+  "check:actions",
+  "check:dockerfile",
   "check:format",
   "check:leaks",
   "check:lint",
+  "check:scan",
   "check:types",
   "check:vuln",
-  "check:scan",
 ]
 
+[tasks."check:actions"]
+description = "Lint and audit GitHub Actions workflows (actionlint + zizmor)"
+run = ["actionlint", "zizmor --offline .github/workflows/"]
+
+[tasks."check:dockerfile"]
+description = "Lint the container image definition (hadolint)"
+run = "hadolint Dockerfile"
+
 [tasks."check:format"]
 description = "Check formatting and config validity"
 run = [
@@ -89,15 +113,18 @@ run = "uv run ty check"
 
 [tasks."check:vuln"]
 description = "Scan dependencies for vulnerabilities (pip-audit)"
-run = "uv run pip-audit"
+run = "uv run pip-audit --skip-editable --cache-dir .cache/pip-audit"
 
 [tasks."check:leaks"]
 description = "Audit codebase for leaked secrets (gitleaks)"
 run = 'gitleaks git --log-opts="--max-count=100" --verbose'
 
 [tasks."check:scan"]
-description = "Scan configuration files for misconfigurations (trivy)"
-run = "trivy config ."
+description = "Scan the checkout for misconfigurations, secrets, and licenses (trivy)"
+# `--config` is explicit on purpose: without it a `TRIVY_CONFIG` exported in the user's
+# shell silently wins over the policy committed here, and the repository's own rules
+# never run. `fs` (not `config`) is what actually enables every scanner in trivy.yaml.
+run = "trivy --config trivy.yaml fs ."
 
 # TEST
 
@@ -106,6 +133,12 @@ alias = "t"
 description = "Run the test suite with coverage (pytest)"
 run = "uv run pytest"
 
+[tasks."test:parallel"]
+description = "Run the test suite across CPU cores, without the coverage gate (pytest-xdist)"
+# Fast local feedback only. pytest-cov and pytest-xdist deadlock on this suite, so the
+# coverage gate stays on the serial `test` task that hooks and CI run.
+run = "uv run pytest -n auto --no-cov"
+
 # BUILD
 
 [tasks.build]
@@ -139,8 +172,11 @@ description = "Run the MLflow environment doctor"
 run = "uv run mlflow doctor"
 
 [tasks."mlflow:serve"]
-description = "Start a local MLflow tracking server"
-run = "uv run mlflow server --host=127.0.0.1 --port=5000 --backend-store-uri=./mlruns"
+description = "Start a local MLflow tracking server on the SQLite backend"
+# Same store the jobs write to: metadata in mlflow.db, artifact files under ./mlruns.
+# The server's artifact root is set explicitly so a served experiment and a directly
+# tracked one land in the same directory instead of MLflow's separate ./mlartifacts.
+run = "uv run mlflow server --host=127.0.0.1 --port=5000 --backend-store-uri=sqlite:///mlflow.db --artifacts-destination=./mlruns"
 
 # PROJECT (MLflow Projects)
 
@@ -189,5 +225,5 @@ description = "Remove caches and build artifacts"
 run = [
   "find . -type f -name '*.py[co]' -delete",
   "find . -type d -name __pycache__ -exec rm -rf {} +",
-  "rm -rf .pytest_cache .ruff_cache .ty_cache .coverage htmlcov dist docs mlruns/* outputs/*",
+  "rm -rf .cache .pytest_cache .ruff_cache .ty_cache .coverage htmlcov dist docs mlflow.db mlartifacts/* mlruns/* outputs/*",
 ]
diff --git a/mlflow.db b/mlflow.db
deleted file mode 100644
index b73d6c0..0000000
Binary files a/mlflow.db and /dev/null differ
diff --git a/pyproject.toml b/pyproject.toml
index 45fba92..0dd1d55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,19 +54,30 @@ bikes = 'bikes.scripts:main'
 
 [dependency-groups]
 dev = [
-  "lefthook>=2.1.9",
+  "lefthook>=2.1.10",
   "pdoc>=16.0.0",
-  "pip-audit>=2.9.0",
+  "pip-audit>=2.10.1",
   "pytest>=9.1.1",
   "pytest-cov>=7.1.0",
-  "pytest-mock>=3.14.0",
-  "pytest-xdist>=3.6.1",
-  "ruff>=0.15.0",
-  "ty>=0.0.56,<0.1", # pre-1.0: pin a compatible range until it stabilizes
+  "pytest-mock>=3.15.1",
+  "pytest-xdist>=3.8.0",
+  # Ruff 0.16 formats Python inside Markdown and rewrote the default rule set:
+  # an older Ruff would disagree with this repository's formatting, so floor it.
+  "ruff>=0.16.2",
+  "ty>=0.0.69,<0.1", # pre-1.0: pin a compatible range until it stabilizes
   "validate-pyproject>=0.25",
 ]
 notebook = ["ipykernel>=6.29.5", "nbformat>=5.10.4"]
 
+# OVERRIDES
+
+[tool.uv]
+# MLflow 3.15 still declares `cryptography<50`, but the fix for PYSEC-2026-3552
+# (PKCS#7 Bleichenbacher oracle) only ships in 50.0.0. Overriding the stale upper
+# bound installs the patched library; the test suite is what proves MLflow still
+# works with it. Drop this override once MLflow relaxes the constraint upstream.
+override-dependencies = ["cryptography>=50"]
+
 # TOOLS
 
 [tool.ruff]
@@ -158,7 +169,9 @@ addopts = [
   "--strict-markers",
   "--cov=src",
   "--cov-report=term-missing",
-  "--cov-fail-under=80",
+  # The suite actually covers every line and branch, so the gate says so: a drop is a
+  # regression to fix, not a threshold to lower.
+  "--cov-fail-under=100",
 ]
 pythonpath = ["src"]
 testpaths = ["tests"]
@@ -176,5 +189,7 @@ skip_covered = true
 # SYSTEMS
 
 [build-system]
-requires = ["uv_build>=0.9.0"]
+# Keep the upper bound at least one minor ahead of the pinned `uv` tool: without it
+# `uv build` warns, and a future breaking `uv_build` would silently break the sdist.
+requires = ["uv_build>=0.9,<0.13"]
 build-backend = "uv_build"
diff --git a/src/bikes/io/services.py b/src/bikes/io/services.py
index 1834dfc..cfcb378 100644
--- a/src/bikes/io/services.py
+++ b/src/bikes/io/services.py
@@ -6,7 +6,6 @@
 
 import abc
 import contextlib as ctx
-import os
 import sys
 import typing as T
 
@@ -171,8 +170,11 @@ class RunConfig(pdt.BaseModel, strict=True, frozen=True, extra="forbid"):
         log_system_metrics: bool | None = True
 
     # server uri
-    tracking_uri: str = "./mlruns"
-    registry_uri: str = "./mlruns"
+    # SQLAlchemy backends are the supported store in MLflow 3: SQLite gives the local
+    # setup the same shape as a production database (Postgres, MySQL) with no server to
+    # run, and it is the only local store the model registry is actually designed for.
+    tracking_uri: str = "sqlite:///mlflow.db"
+    registry_uri: str = "sqlite:///mlflow.db"
     # experiment
     experiment_name: str = "bikes"
     # registry
@@ -189,10 +191,6 @@ class RunConfig(pdt.BaseModel, strict=True, frozen=True, extra="forbid"):
 
     @T.override
     def start(self) -> None:
-        # MLflow 3 puts the filesystem store (e.g., './mlruns') in maintenance mode and
-        # refuses it by default. Opt in for the simple local backend used in development.
-        # In production, prefer a database backend (e.g., 'sqlite:///mlflow.db').
-        os.environ.setdefault("MLFLOW_ALLOW_FILE_STORE", "true")
         # server uri
         mlflow.set_tracking_uri(uri=self.tracking_uri)
         mlflow.set_registry_uri(uri=self.registry_uri)
diff --git a/tests/conftest.py b/tests/conftest.py
index 8fd1056..530c70c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -3,6 +3,7 @@
 # %% IMPORTS
 
 import os
+import shutil
 import typing as T
 
 import omegaconf
@@ -93,8 +94,8 @@ def extra_config() -> str:
                 "enable": false,
             },
             "mlflow_service": {
-                "tracking_uri": "${tmp_path:}/tracking/",
-                "registry_uri": "${tmp_path:}/registry/",
+                "tracking_uri": "sqlite:///${tmp_path:}/mlflow.db",
+                "registry_uri": "sqlite:///${tmp_path:}/mlflow.db",
             }
         }
     }
@@ -310,12 +311,34 @@ def alerts_service() -> T.Generator[services.AlertsService]:
     service.stop()
 
 
+@pytest.fixture(scope="session")
+def mlflow_db_template(tmp_path_factory: pytest.TempPathFactory) -> str:
+    """Return a migrated but empty MLflow database used as a template by every test.
+
+    Creating an MLflow SQLite store runs its Alembic migrations, which costs seconds.
+    Paying that once per session and copying the file per test keeps the isolation of a
+    fresh database at the cost of a file copy.
+    """
+    path = tmp_path_factory.mktemp("mlflow") / "template.db"
+    services.MlflowService(
+        tracking_uri=f"sqlite:///{path}",
+        registry_uri=f"sqlite:///{path}",
+        experiment_name="Experiment-Template",
+        registry_name="Registry-Template",
+    ).start()
+    return str(path)
+
+
 @pytest.fixture(scope="function", autouse=True)
-def mlflow_service(tmp_path: str) -> T.Generator[services.MlflowService]:
+def mlflow_service(tmp_path: str, mlflow_db_template: str) -> T.Generator[services.MlflowService]:
     """Return and start the mlflow service."""
+    # Each test gets its own SQLite file under tmp_path, so runs stay isolated the same
+    # way the old per-test directories were, with the store the package actually ships.
+    database = os.path.join(tmp_path, "mlflow.db")
+    shutil.copyfile(mlflow_db_template, database)
     service = services.MlflowService(
-        tracking_uri=f"{tmp_path}/tracking/",
-        registry_uri=f"{tmp_path}/registry/",
+        tracking_uri=f"sqlite:///{database}",
+        registry_uri=f"sqlite:///{database}",
         experiment_name="Experiment-Testing",
         registry_name="Registry-Testing",
     )
diff --git a/uv.lock b/uv.lock
index 75cbfa6..21a19e4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,6 +6,9 @@ resolution-markers = [
     "platform_machine != 'x86_64' or sys_platform != 'darwin'",
 ]
 
+[manifest]
+overrides = [{ name = "cryptography", specifier = ">=50" }]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.7.1"
@@ -17,7 +20,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.14.1"
+version = "3.14.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -28,49 +31,49 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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