feat!: migrate to canonical stack — v5.0.0 (Python 3.14, MLflow 3)

[fmind]

What

Migrate to the canonical fmind stack and bump to v5.0.0.

Why

Adopt current standards (mise, lefthook, ty, dprint, git-cliff, uv_build) and the latest majors (Python 3.14, MLflow 3.14, pandas 2.3, scikit-learn 1.9, pandera 0.32).

How

• Toolchain: justfile+tasks → mise; pre-commit → lefthook; mypy → ty; bandit → Ruff S; commitizen → git-cliff; hatchling → uv_build; + dprint/trivy/pip-audit.
• Code: pandera.pandas namespace; MLflow 3 name= / validate_evaluation_results; sklearn __sklearn_tags__; MLflow-3 file-store opt-in.
• CI/CD: mise-based CI; release CD (GHCR image + Actions Pages docs). Dockerfile multi-stage 3.14, non-root.

Test plan

mise run format/check/test green (45 passed, 100% coverage), docker image builds+runs, live MLflow Projects run succeeds.

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly upgrades the project's foundational technologies and development workflow. It introduces a new, standardized MLOps stack, updates core dependencies to their latest major versions, and refines the build, testing, and deployment processes. The changes aim to enhance maintainability, leverage modern tooling, and ensure compatibility with the latest ecosystem standards, particularly for Python and MLflow. This migration streamlines development tasks and improves the overall robustness and efficiency of the project.

Highlights

• Toolchain Migration: The project has migrated to a new canonical stack, replacing justfile with mise for task automation, pre-commit with lefthook for Git hooks, mypy with ty for type checking, bandit with Ruff's security rules, commitizen with git-cliff for changelog generation, and hatchling with uv_build for package building. New tools like dprint, trivy, and pip-audit have also been integrated.
• Version Updates: The project now targets Python 3.14 and MLflow 3.14.0, along with updated major versions for pandas (2.3), scikit-learn (1.9), and pandera (0.32). The project version has been bumped to v5.0.0.
• MLflow 3 Compatibility: Adjustments were made to accommodate MLflow 3's changes, including using the name= argument for logging models, adopting mlflow.validate_evaluation_results for metric validation, and explicitly opting into the local filesystem store (MLFLOW_ALLOW_FILE_STORE=true) for development environments.
• Code Modernization and Refinements: Pandera imports were updated to use the pandera.pandas namespace. Scikit-learn models now include __sklearn_tags__ for better compatibility with meta-estimators. Various code snippets in notebooks and test files were reformatted for brevity and clarity.
• CI/CD and Docker Enhancements: The CI/CD pipelines have been reconfigured to be mise-based, and a new multi-stage, non-root Dockerfile using Python 3.14 has been introduced for improved containerization and security.
• Documentation for AI Agents: A new AGENTS.md file has been added, providing a comprehensive overview of the project, setup instructions, core commands, definition of done, conventions, and repository layout specifically tailored for AI agents contributing to the repository.

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Ignored Files

• Ignored by pattern: .gemini/** (7)
  • .gemini/skills/MLOps Automation/SKILL.md
  • .gemini/skills/MLOps Collaboration/SKILL.md
  • .gemini/skills/MLOps Industrialization/SKILL.md
  • .gemini/skills/MLOps Initialization/SKILL.md
  • .gemini/skills/MLOps Observability/SKILL.md
  • .gemini/skills/MLOps Prototyping/SKILL.md
  • .gemini/skills/MLOps Validation/SKILL.md
• Ignored by pattern: .github/workflows/** (4)
  • .github/workflows/cd.yml
  • .github/workflows/check.yml
  • .github/workflows/ci.yml
  • .github/workflows/publish.yml

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[gemini-code-assist]

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