Production Ready Machine Learning
21 mei, 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?
Diego will gladly help you further with custom training solutions.
Diego Teunissen
Data and AI Training Advisor
Duur
3 days
Tijd
09:00 – 17:00
Taal
English
Lunch
Included
Certificering
No
Level
Professional
What will you learn?
After the training, you will be able to:
Understand what it means for a project to be production-ready
Write robust Python code that is easy to extend, debug, monitor and test
Refactor code from notebooks into a high-quality Python package with Poetry for your machine learning projects that is easy to share, collaborate and deploy
Serve your models with APIs and CLIs
Program
This training combines conceptual explanations, practical exercises, and a capstone project, which touch upon the most relevant aspects of production-ready applications. If you follow this training, 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 is production-ready code?
- Best practices for code organization: going from jupyter notebooks to using and developing packages with poetry
- Writing high quality code and implementing automatic quality checks with ruff and pre-commit
- Object-oriented programming (OOP) in python
This training is for you if:
You want to know how to refactor code from notebooks into mature Python packages
You want to enhance the quality of your code and use current industry-standard tools
You want to be able to collaborate better on projects with your colleagues
You are a Data Scientist, Analysts or Engineer and spend your working hours developing Python-based solutions
This training is not for you if:
You don’t have basic Python experience, which is required (check out our Python for Data Analysis course instead)
You have never used Git or you have never used a shell/terminal before.
You are looking to enhance your machine learning knowledge (check out the Certified Data Science with Python or the Advanced Data Science with Python trainings)
You want to study in-depth about data pipelines or about deploying on specific cloud environments (check out the ML System Design or MLOps trainings)
Meet the Trainers
David Coba
David has a background in mathematical psychology, where he used statistics and data science to study human behavior. He also has a passion for software development and multiple years of experience teaching about technical topics.
Nitin Bhushan
Meet Nitin Bhushan, trainer at Xebia Data. Nitin is a data scientist and teaches many data and AI related skills.
Enrico Erler
Meet Enrico, a data science trainer at Xebia Academy with a passion for Deep Learning and Statistics. Get to know him here.
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