This repository contains a Python code base with best practices designed to support your MLOps initiatives.

The package leverages several tools and tips to make your MLOps experience as flexible, robust, productive as possible.

You can use this package as part of your MLOps toolkit or platform (e.g., Model Registry, Experiment Tracking, Realtime Inference, ...).

Related Resources:

• LLMOps Coding Package (Example): Example with best practices and tools to support your LLMOps projects.
• MLOps Coding Course (Learning): Learn how to create, develop, and maintain a state-of-the-art MLOps code base.
• Cookiecutter MLOps Package (Template): Start building and deploying Python packages and Docker images for MLOps tasks.
• Agent Skills (Resource): Enhance your AI Agents with standardized skills for MLOps and coding.

• MLOps Python Package
• Table of Contents
• Install
  • Prerequisites
  • Installation
  • Next Steps
• Usage
  • Configuration
  • Execution
  • Automation
  • Workflows
• Tools
  • Automation
    • AI Assistant: Gemini Code Assist
    • Commits: git-cliff
    • Dependabot: Dependabot
    • Git Hooks: Lefthook
    • Tasks: Mise
  • CI/CD
    • Runner: GitHub Actions
    • Workflow Linting: actionlint + zizmor
  • CLI
    • Parser: Argparse
    • Logging: Loguru
  • Code
    • Coverage: Coverage
    • Editor: VS Code
    • Formatting: Ruff
    • Quality: Ruff
    • Security: Ruff
    • Testing: Pytest
    • Typing: ty
    • Versioning: Git
  • Configs
    • Format: YAML
    • Parser: OmegaConf
    • Reader: Cloudpathlib
    • Validator: Pydantic
    • Formatter: dprint
  • Data
    • Container: Pandas
    • Format: Parquet
    • Schema: Pandera
  • Docs
    • API: pdoc
    • Format: Google
    • Hosting: GitHub Pages
  • Model
    • Evaluation: Scikit-Learn Metrics
    • Format: Mlflow Model
    • Registry: Mlflow Registry
    • Tracking: Mlflow Tracking
  • Package
    • Evolution: Changelog
    • Format: Wheel
    • Manager: uv
    • Runtime: Docker
    • Image Linting: hadolint
  • Programming
    • Language: Python
    • Version: Uv
  • Observability
    • Reproducibility: Mlflow Project
    • Monitoring : Mlflow Evaluate
    • Alerting: Plyer
    • Lineage: Mlflow Dataset
    • Explainability: SHAP
    • Infrastructure: Mlflow System Metrics
• Tips
  • AI/ML Practices
    • Data Catalog
    • Hyperparameter Optimization
    • Data Splits
  • Design Patterns
    • Directed-Acyclic Graph
    • Program Service
    • Soft Coding
    • SOLID Principles
    • IO Separation
  • Python Powers
    • Context Manager
    • Python Package
  • Software Engineering
    • Code Typing
    • Config Typing
    • Dataframe Typing
    • Object Oriented
    • Semantic Versioning
  • Testing Tricks
    • Parallel Testing
    • Test Fixtures
  • VS Code
    • Code Workspace
    • GitHub Copilot
    • VSCode VIM
• Resources
  • Python
  • AI/ML/MLOps

This section details the requirements, actions, and next steps to kickstart your MLOps project.

• Python>=3.14: to benefit from the latest features and performance improvements
• uv>=0.12: to initialize the project virtual environment and its dependencies
• mise: to run the canonical project tasks (install, format, check, test, build, and all) shared by git hooks and CI

1. Clone this GitHub repository on your computer

# with ssh (recommended)
$ git clone git@github.com:fmind/mlops-python-package
# with https
$ git clone https://github.com/fmind/mlops-python-package

2. Run the project installation with mise

cd mlops-python-package/
mise run install

3. Adapt the code base to your desire

Going from there, there are dozens of ways to integrate this package to your MLOps platform.

For instance, you can use Databricks or AWS as your compute platform and model registry.

It's up to you to adapt the package code to the solution you target. Good luck champ!

This section explains how configure the project code and execute it on your system.

You can add or edit config files in the confs/ folder to change the program behavior.

# confs/training.yaml
job:
  KIND: TrainingJob
  inputs:
    KIND: ParquetReader
    path: data/inputs_train.parquet
  targets:
    KIND: ParquetReader
    path: data/targets_train.parquet

This config file instructs the program to start a TrainingJob with 2 parameters:

• inputs: dataset that contains the model inputs
• targets: dataset that contains the model target

You can find all the parameters of your program in the src/[package]/jobs/*.py files.

You can also print the full schema supported by this package using uv run bikes --schema.

The project code can be executed with uv during your development:

uv run [package] confs/tuning.yaml
uv run [package] confs/training.yaml
uv run [package] confs/promotion.yaml
uv run [package] confs/inference.yaml
uv run [package] confs/evaluations.yaml
uv run [package] confs/explanations.yaml

In production, you can build, ship, and run the project as a Python package:

uv build
uv publish # optional
python -m pip install [package]
[package] confs/inference.yaml

You can also install and use this package as a library for another AI/ML project:

from [package] import jobs

job = jobs.TrainingJob(...)
with job as runner:
    runner.run()

Additional tips:

• You can pass extra configs from the command line using the --extras flag
  • Use it to pass runtime values (e.g., a result from previous job executions)
• You can pass several config files in the command-line to merge them from left to right
  • You can define common configurations shared between jobs (e.g., model params)
• The right job task will be selected automatically thanks to Pydantic Discriminated Unions
  • This is a great way to run any job supported by the application (training, tuning, ...)

This project includes several automation tasks to easily repeat common actions.

AI agents: read AGENTS.md for the full stack, task vocabulary, and conventions before contributing.

You can run the tasks from the command-line or VS Code extension.

# execute the project DAG
$ mise run project
# build a code archive
$ mise run build
# list other actions
$ mise tasks

Available tasks:

$ mise tasks
install          # Install dependencies and git hooks
install:python   # Sync Python dependencies (uv)
install:hooks    # Install git hooks (lefthook)
install:rulesets # Install GitHub branch rulesets
format           # Format all sources and documents
format:python    # Format Python sources and imports (ruff)
format:dprint    # Format JSON, Markdown, TOML, YAML (dprint)
check            # Run all static checks in parallel
check:format     # Check formatting and config validity
check:lint       # Lint Python sources (ruff)
check:types      # Type-check Python sources (ty)
check:vuln       # Scan dependencies for vulnerabilities (pip-audit)
check:leaks      # Audit codebase for leaked secrets (gitleaks)
check:scan       # Scan configuration files for misconfigurations (trivy)
test             # Run the test suite with coverage (pytest)
build            # Build the Python distribution (wheel + sdist)
build:python     # Build Python distribution artifacts (uv)
build:image      # Build the production OCI image (Docker)
docs             # Generate the API documentation (pdoc)
docs:serve       # Serve the API documentation (pdoc)
mlflow:doctor    # Run the MLflow environment doctor
mlflow:serve     # Start a local MLflow tracking server
project          # Run every MLflow job in sequence
project:run      # Run one MLflow job by config name (e.g. project:run training)
docker:compose   # Start the local MLflow server with Docker Compose
docker:run       # Run the latest built image
coverage         # Open the HTML coverage report
upgrade          # Upgrade dependencies to the latest compatible versions
clean            # Remove caches and build artifacts

This package supports two GitHub Workflows in .github/workflows:

• ci.yml: run format, check, and test on each push and Pull Request
• cd.yml: build and publish the docs (GitHub Pages) and the Docker image on code release.

You can use and extend these workflows to automate repetitive package management tasks.

This sections motivates the use of developer tools to improve your coding experience.

Pre-defined actions to automate your project development.

• Motivations:
  • Increase your coding productivity
  • Get code suggestions and completions
  • Reduce the time spent on reviewing code
• Limitations:
  • Can generate wrong code, reviews, or summaries

• Motivations:
  • Standardize commits with Conventional Commits
  • Generate a standard changelog from the git history
  • Integrate well with SemVer and PEP 440
• Limitations:
  • Requires disciplined commit messages
• Alternatives:
  • Do It Yourself (DIY)

• Motivations:
  • Avoid security issues
  • Avoid breaking changes
  • Update your dependencies
• Limitations:
  • Can break your code
• Alternatives:
  • Do It Yourself (DIY)

• Motivations:
  • Check your code locally before a commit
  • Avoid wasting resources on your CI/CD
  • Fast, parallel hooks that delegate to mise run tasks
• Limitations:
  • Add overhead before your commit
• Alternatives:
  • Git Hooks: native but less convenient to use

• Motivations:
  • Automate project workflows
  • One task vocabulary shared by git hooks and CI
  • Also manages the project toolchain (uv, dprint, gitleaks, trivy)
• Limitations:
  • Not familiar to most developers
• Alternatives:
  • Make: most popular, but awful syntax
  • Task: YAML task runner without toolchain management

Execution of automated workflows on code push and releases.

• Motivations:
  • 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: can be installed on-premise

• 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

Integrations with the Command-Line Interface (CLI) of your system.

• Motivations:
  • Provide CLI arguments
  • Included in Python runtime
  • Sufficient for providing configs
• Limitations:
  • More verbose for advanced parsing
• Alternatives:
  • Typer: code typing for the win
  • Fire: simple but no typing
  • Click: more verbose

• Motivations:
  • Show progress to the user
  • Work fine out of the box
  • Saner logging syntax
• Limitations:
  • Doesn't let you deviate from the base usage
• Alternatives:
  • Logging: available by default, but feel dated

Edition, validation, and versioning of your project source code.

• Motivations:
  • Report code covered by tests
  • Identify code path to test
  • Show maturity to users
• Limitations:
  • None
• Alternatives:
• Pytest Cov A Pytest plugin that uses coverage.py to measure code coverage.

• Motivations:
  • Open source
  • Free, simple, open source
  • Great plugins for Python development
• Limitations:
  • Require some configuration for Python
• Alternatives:
  • PyCharm: provide a lot, cost a lot
  • Vim: I love it, but there is a VS Code plugin
  • Spacemacs: I love it even more, but not everybody loves LISP

• Motivations:
  • Super fast compared to others
  • Don't waste time arranging your code
  • Make your code more readable/maintainable
• Limitations:
  • Still in version 0.x, but more and more adopted
• Alternatives:
  • YAPF: more config options that you don't need
  • Isort + Black: slower and need two tools

• Motivations:
  • Improve your code quality
  • Super fast compared to others
  • Great integration with VS Code
• Limitations:
  • None
• Alternatives:
  • PyLint: too slow and too complex system
  • Flake8: too much plugins, I prefer Pylint in practice

• Motivations:
  • Detect security issues with the S (flake8-bandit) rules
  • Complemented by Trivy, gitleaks, and pip-audit in mise run check
  • No extra tool to install: folded into the linter
• Limitations:
  • None
• Alternatives:
  • Bandit: the standalone tool the S rules are based on

• Motivations:
  • Write tests or pay the price
  • Super easy to write new test cases
  • Tons of good plugins (xdist, sugar, cov, ...)
• Limitations:
  • Doesn't support parallel execution out of the box
• Alternatives:
  • Unittest: more verbose, less fun

• Motivations:
  • Static typing is cool!
  • Extremely fast, written in Rust by Astral
  • Same maintainers and ecosystem as Ruff and uv
• Limitations:
  • Pre-1.0, still stabilizing its type-checking rules
• Alternatives:
  • PyRight: check big code base by MicroSoft
  • PyType: check big code base by Google
  • Pyre: check big code base by Facebook

• Motivations:
  • If you don't version your code, you are a fool
  • Most popular source code manager (what else?)
  • Provide hooks to perform automation on some events
• Limitations:
  • Git can be hard: https://xkcd.com/1597/
• Alternatives:
  • Mercurial: loved it back then, but git is the only real option

Manage the configs files of your project to change executions.

• Motivations:
  • Change execution without changing code
  • Readable syntax, support comments
  • Allow to use OmegaConf <3
• Limitations:
  • Not supported out of the box by Python
• Alternatives:
  • JSON: no comments, more verbose
  • TOML: less suited to config merge/sharing

• Motivations:
  • Parse and merge YAML files
  • Powerful, doesn't get in your way
  • Achieve a lot with few lines of code
• Limitations:
  • Do not support remote files (e.g., s3, gcs, ...)
    • You can combine it with cloudpathlib
• Alternatives:
  • Hydra: powerful, but gets in your way
  • DynaConf: more suited for app development

• Motivations:
  • Read files from cloud storage
  • Better integration with cloud platforms
  • Support several platforms: AWS, GCP, and Azure
• Limitations:
  • Support of Python typing is not great at the moment
• Alternatives:
  • Cloud SDK (GCP, AWS, Azure, ...): vendor specific, overkill for this task

• Motivations:
  • Validate your config before execution
  • Pydantic should be builtin (period)
  • Super charge your Python class
• Limitations:
  • None
• Alternatives:
  • Dataclass: simpler, but much less powerful
  • Attrs: no validation, less intuitive to use

• Motivations:
  • Format JSON, Markdown, TOML, and YAML files
  • Fast, pluggable formatter written in Rust
  • Keep configs and docs consistent alongside Ruff for Python
• Limitations:
  • Separate tool from the Python formatter (Ruff)
• Alternatives:
  • Prettier: broader language support, but slower and Node-based

Define the datasets to provide data inputs and outputs.

• Motivations:
  • Load data files in memory
  • Lingua franca for Python
  • Most popular options
• Limitations:
  • Lot of gotchas
• Alternatives:
  • Polars: faster, saner, but less integrations
  • Pyspark: powerful, popular, distributed, so much overhead
  • Dask, Ray, Modin, Vaex, ...: less integration (even if it looks like pandas)

• Motivations:
  • Store your data on disk
  • Column-oriented (good for analysis)
  • Much more efficient and saner than text based
• Limitations:
  • None
• Alternatives:
  • CSV: human readable, but that's the sole benefit
  • Avro: good alternative for row-oriented workflow

• Motivations:
  • Typing for dataframe
  • Communicate data fields
  • Support pandas and others
• Limitations:
  • None
• Alternatives:
  • Great Expectations: powerful, but much more difficult to integrate

Generate and share the project documentations.

• Motivations:
  • Share docs with others
  • Simple tool, only does API docs
  • Get the job done, get out of your way
• Limitations:
  • Only support API docs (i.e., no custom docs)
• Alternatives:
  • Sphinx: More complete, overkill for simple projects
  • Mkdocs: More complete, but requires more setup

• Motivations:
  • Common style for docstrings
  • Most writeable out of alternatives
  • I often write a single line for simplicity
• Limitations:
  • None
• Alternatives:
  • Numpy: less writeable
  • Sphinx: baroque style

• Motivations:
  • Easy to setup
  • Free and simple
  • Integrated with GitHub
• Limitations:
  • Only support static content
• Alternatives:
  • ReadTheDocs: provide more features

Toolkit to handle machine learning models.

• Motivations:
  • Bring common metrics
  • Avoid reinventing the wheel
  • Avoid implementation mistakes
• Limitations:
  • Limited set of metric to be chosen
• Alternatives:
  • Implement your own: for custom metrics

• Motivations:
  • Standard ML format
  • Store model dependencies
  • Strong community ecosystem
• Limitations:
  • None
• Alternatives:
  • Pickle: work out of the box, but less suited for big array
  • ONNX: great for deep learning, no guaranteed compatibility for the rest

• Motivations:
  • Save and load models
  • Separate production from consumption
  • Popular, open source, work on local system
• Limitations:
  • None
• Alternatives:
  • Neptune.ai: SaaS solution
  • Weights and Biases: SaaS solution

• Motivations:
  • Keep track of metrics and params
  • Allow to compare model performances
  • Popular, open source, work on local system
• Limitations:
  • None
• Alternatives:
  • Neptune.ai: SaaS solution
  • Weights and Biases: SaaS solution

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.

Define and build modern Python package.

• Motivation:
  • Communicate changes to user
  • Generated from Conventional Commits with git-cliff
  • Standardized with Keep a Changelog
• Limitations:
  • None
• Alternatives:
  • None

• Motivations:
  • Has several advantages
  • Create source code archive
  • Most modern Python format
• Limitations:
  • Doesn't ship with C/C++ dependencies (e.g., CUDA)
    • i.e., use Docker containers for this case
• Alternatives:
  • Source: older format, less powerful
  • Conda: slow and hard to manage

• Motivations:
  • Define and build Python package (with the uv_build backend)
  • Fast and compliant package manager
  • Pack every metadata in a single static file
• Limitations:
  • Cannot add dependencies beyond Python (e.g., CUDA)
    • i.e., use Docker container for this use case
• Alternatives:
  • Setuptools: dynamic file is slower and more risky
  • Poetry: previous solution of this package
  • Pdm, Hatch, PipEnv: https://xkcd.com/1987/

• Motivations:
  • Create isolated runtime
  • Container is the de facto standard
  • Package C/C++ dependencies with your project
• Limitations:
  • Some company might block Docker Desktop, you should use alternatives
• Alternatives:
  • Conda: slow and heavy resolver

• 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 page every time

Select your programming environment.

• Motivations:
  • Great language for AI/ML projects
  • Robust with additional tools
  • Hundreds of great libs
• Limitations:
  • Slow without C bindings
• Alternatives:
  • R: specific purpose language
  • Julia: specific purpose language

• Motivations:
  • Switch between Python version
  • Allow to select the best version
  • Support global and local dispatch
• Limitations:
  • Require some shell configurations
• Alternatives:
  • Manual installation: time consuming
  • PyEnv: shell-based, require more setup

• Motivations:
  • Share common project formats.
  • Ensure the project can be reused.
  • Avoid randomness in project execution.
• Limitations:
  • Mlflow Project is best suited for small projects.
• Alternatives:
  • DVC: both data and models.
  • Metaflow: focus on machine learning.
  • Apache Airflow: for large scale projects.

Run an MLflow Project job with mise run project:run <job> (e.g. mise run project:run training); it passes --env-manager=local to reuse the uv-managed environment instead of creating a new one.

• Motivations:
  • Compute the model metrics.
  • Validate model with thresholds.
  • Perform post-training evaluations.
• Limitations:
  • Mlflow Evaluate is less feature-rich as alternatives.
• Alternatives:
  • Giskard: open-core and super complete.
  • Evidently: open-source with more metrics.
  • Arize AI: more feature-rich but less flexible.
  • Graphana: you must do everything yourself.

• Motivations:
  • Simple solution.
  • Send notifications on system.
  • Cross-system: Mac, Linux, Windows.
• Limitations:
  • Should not be used for large scale projects.
• Alternatives:
  • Slack: for chat-oriented solutions.
  • Datadog: for infrastructure oriented solutions.

• Motivations:
  • Store information in Mlflow.
  • Track metadata about run datasets.
  • Keep URI of the dataset source (e.g., website).
• Limitations:
  • Not as feature-rich as alternative solutions.
• Alternatives:
  • Databricks Lineage: limited to Databricks.
  • OpenLineage and Marquez: open-source and flexible.

• Motivations:
  • Most popular toolkit.
  • Support various models (linear, model, ...).
  • Integration with Mlflow through the SHAP module.
• Limitations:
  • Super slow on large dataset.
  • Mlflow SHAP module is not mature enough.
• Alternatives:
  • LIME: not maintained anymore.

• Motivations:
  • Track infrastructure information (RAM, CPU, ...).
  • Integrated with Mlflow tracking.
  • Provide hardware insights.
• Limitations:
  • Not as mature as alternative solutions.
• Alternatives:
  • Datadog: popular and mature solution.

This sections gives some tips and tricks to enrich the develop experience.

You should decouple the pointer to your data from how to access it.

In your code, you can refer to your dataset with a tag (e.g., inputs, targets).

This tag can then be associated to a reader/writer implementation in a configuration file:

inputs:
  KIND: ParquetReader
  path: data/inputs_train.parquet
targets:
  KIND: ParquetReader
  path: data/targets_train.parquet

In this package, the implementation are described in src/[package]/io/datasets.py and selected by KIND.

You should select the best hyperparameters for your model using optimization search.

The simplest projects can use a sklearn.model_selection.GridSearchCV to scan the whole search space.

This package provides a simple interface to this hyperparameter search facility in src/[package]/utils/searchers.py.

For more complex project, we recommend to use more complex strategy (e.g., Bayesian) and software package (e.g., Optuna).

You should properly split your dataset into a training, validation, and testing sets.

• Training: used for fitting the model parameters
• Validation: used to find the best hyperparameters
• Testing: used to evaluate the final model performance

The sets should be exclusive, and the testing set should never be used as training inputs!

This package provides a simple deterministic strategy implemented in src/[package]/utils/splitters.py.

You should use Directed-Acyclic Graph (DAG) to connect the steps of your ML pipeline.

A DAG can express the dependencies between steps while keeping the individual step independent.

This package provides a DAG example in the project task of mise.toml. The approach is based on mise and is explained in the section on Automation above.

In production, we recommend to use a scalable system such as Airflow, Dagster, Prefect, Metaflow, or ZenML.

You should provide a global context for the execution of your program.

There are several approaches such as Singleton, Global Variable, or Component.

This package takes inspiration from Clojure mount. It provides an implementation in src/[package]/io/services.py.

You should separate the program implementation from the program configuration.

Exposing configurations to users allow them to influence the execution behavior without code changes.

This package seeks to expose as much parameter as possible to the users in configurations stored in the confs/ folder.

You should implement the SOLID principles to make your code as flexible as possible.

• Single responsibility principle: Class has one job to do. Each change in requirements can be done by changing exactly one class.
• Open/closed principle: Class is happy (open) to be used by others. Class is not happy (closed) to be changed by others.
• Liskov substitution principle: Class can be replaced by any of its children. Children classes inherit parent's behaviours.
• Interface segregation principle: When classes promise each other something, they should separate these promises (interfaces) into many small promises, so it's easier to understand.
• Dependency inversion principle: When classes talk to each other in a very specific way, they both depend on each other to never change. Instead classes should use promises (interfaces, parents), so classes can change as long as they keep the promise.

In practice, this mean you can implement software contracts with interface and swap the implementation.

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.

You should separate the code interacting with the external world from the rest.

The external is messy and full of risks: missing files, permission issue, out of disk ...

To isolate these risks, you can put all the related code in an io package and use interfaces

You should use Python context manager to control and enhance an execution.

Python provides contexts that can be used to extend a code block. For instance:

# in src/[package]/scripts.py
with job as runner:  # context
    runner.run()  # run in context

This pattern has the same benefit as Monad, a powerful programming pattern.

The package uses src/[package]/jobs/*.py to handle exception and services.

You should create Python package to create both library and application for others.

Using Python package for your AI/ML project has the following benefits:

• Build code archive (i.e., wheel) that be uploaded to Pypi.org
• Install Python package as a library (e.g., like pandas)
• Expose script entry points to run a CLI or a GUI

To build a Python package with uv, you simply have to type in a terminal:

# for all uv project
uv build
# for this project only
mise run build

You should type your Python code to make it more robust and explicit for your user.

Python provides the typing module for adding type hints and ty to checking them.

# in src/[package]/core/models.py
@abc.abstractmethod
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."""

This code snippet clearly state the inputs and outputs of the method, both for the developer and the type checker.

The package aims to type every functions and classes to facilitate the developer experience and fix mistakes before execution.

You should type your configuration to avoid exceptions during the program execution.

Pydantic allows to define classes that can validate your configs during the program startup.

# in src/[package]/utils/splitters.py
class TrainTestSplitter(Splitter):
    shuffle: bool = False  # required (time sensitive)
    test_size: int | float = 24 * 30 * 2  # 2 months
    random_state: int = 42

This code snippet allows to communicate the values expected and avoid error that could be avoided.

The package combines both OmegaConf and Pydantic to parse YAML files and validate them as soon as possible.

You should type your dataframe to communicate and validate their fields.

Pandera supports dataframe typing for Pandas and other library like PySpark:

# in src/package/schemas.py
class InputsSchema(Schema):
    instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True)
    dteday: papd.Series[papd.DateTime] = pa.Field()
    season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4])
    yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1)
    mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12)
    hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23)
    holiday: papd.Series[papd.Bool] = pa.Field()
    weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6)
    workingday: papd.Series[papd.Bool] = pa.Field()
    weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4)
    temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
    atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
    hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
    windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1)
    casual: papd.Series[papd.UInt32] = pa.Field(ge=0)
    registered: papd.Series[papd.UInt32] = pa.Field(ge=0)

This code snippet defines the fields of the dataframe and some of its constraint.

The package encourages to type every dataframe used in src/[package]/core/schemas.py.

You should use the Objected Oriented programming to benefit from polymorphism.

Polymorphism combined with SOLID Principles allows to easily swap your code components.

class Reader(abc.ABC, pdt.BaseModel):
    @abc.abstractmethod
    def read(self) -> pd.DataFrame:
        """Read a dataframe from a dataset."""

This code snippet uses the abc module to define code interfaces for a dataset with a read/write method.

The package defines class interface whenever possible to provide intuitive and replaceable parts for your AI/ML project.

You should use semantic versioning to communicate the level of compatibility of your releases.

Semantic Versioning (SemVer) provides a simple schema to communicate code changes. For package X.Y.Z:

• Major (X): major release with breaking changed (i.e., imply actions from the benefit)
• Minor (Y): minor release with new features (i.e., provide new capabilities)
• Patch (Z): patch release to fix bugs (i.e., correct wrong behavior)

Uv and this package leverage Semantic Versioning to let developers control the speed of adoption for new releases.

You can run your tests in parallel to speed up the validation of your code base.

Pytest can be extended with the pytest-xdist plugin for this purpose.

This package enables Pytest in its automation tasks by default.

You should define reusable objects and actions for your tests with fixtures.

Fixture can prepare objects for your test cases, such as dataframes, models, files.

This package defines fixtures in tests/conftest.py to improve your testing experience.

You can use VS Code workspace to define configurations for your project.

Code Workspace can enable features (e.g. formatting) and set the default interpreter.

{
 "settings": {
  "editor.formatOnSave": true,
  "python.defaultInterpreterPath": ".venv/bin/python",
    ...
 },
}

This package defines a workspace file that you can load from [package].code-workspace.

You can use GitHub Copilot to increase your coding productivity by 30%.

GitHub Copilot has been a huge productivity thanks to its smart completion.

You should become familiar with the solution in less than a single coding session.

You can use VIM keybindings to more efficiently navigate and modify your code.

Learning VIM is one of the best investment for a career in IT. It can make you 30% more productive.

Compared to GitHub Copilot, VIM can take much more time to master. You can expect a ROI in less than a month.

This section provides resources for building packages for Python and AI/ML/MLOps.

• https://github.com/krzjoa/awesome-python-data-science#readme
• https://github.com/ml-tooling/best-of-ml-python
• https://github.com/ml-tooling/best-of-python
• https://github.com/ml-tooling/best-of-python-dev
• https://github.com/vinta/awesome-python

• https://github.com/josephmisiti/awesome-machine-learning
• https://github.com/visenger/awesome-mlops