Production Ready Machine Learning
31 January, 2024 – Amsterdam, The Netherlands
This practical three-day training will give you the skills to bring your machine-learning models into production. We will teach you how to go from notebooks to packages. You will learn best practices for managing your code, including advanced Python features that help Data and Machine Learning (ML) Engineers make sure their code is readable, maintainable, and scalable.
Looking to upskill your team(s) or organization?
Anuu will gladly help you further with custom training solutions.
Anuudari Bayartsogt
Tech Training Advisor
Duration
3 days
Time
09:00 – 17:00
Language
English
Lunch
Included
Certification
No
Level
Professional
What will you learn?
After the training, you will be able to
Write robust Python code that is easy to extend, debug, monitor, and test.
Create a high-quality Python package for your machine learning project that is easy to share, collaborate, and deploy.
Understand the importance of and what it means for a project to be production-ready.
Serve your models with APIs or CLIs.
Key takeaways
Code quality:
- When is a project production ready?
- Characteristics of high-quality and maintainable code.
- Automatic linting and code formatting with black, ruff.
- Type hinting and type checking with mypy.
Code organization:
- Python version management, package managers, and virtual environments with poetry and pyenv.
- What to test in a data science project with pytest.
- Ensure quality checks on every commit with pre-commit.
- Effective logging and monitoring with logging.
- Build beautiful documentation with Sphinx and MyST.
Python design patterns:
- Object-oriented programming (OOP).
- Dunder methods, iterators, generators.
- Decorators.
Serving your Python programs
- Create a command-line interface to your package with typer.
- Build an API with the modern, fast, high-performance web framework FastAPI.
Program
This Machine Learning training course combines conceptual explanations, practical exercises, and a capstone project, which touches upon most of the relevant aspects of production-ready applications. If you follow this training course, you will learn in a very interactive setting the most modern approaches and best practices to develop machine learning code in a robust, safe, scalable, and easy-to-maintain way.
- What makes a project ‘production-ready’?
- From notebook to Python package.
- How to enhance the quality and robustness of your code.
Who is it for?
This course is for Data Scientists, Analysts, or Engineers working with Python. Do you want to migrate your code from notebooks to mature Python packages, enhance the quality of your code and use current industry-standard tools, or collaborate better on projects with your colleagues to let projects evolve to a more scalable code architecture? Then this course is for you!
Requirements
Basic Python experience is required.
No Python skills yet? Check out the Data Science with Python – Foundation course!
What else
should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements and share literature if you need to prepare.
See you soon!
Course information
All literature and course materials are included in the price.
After registering for this course, you will receive a confirmation email with practical information.
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