ML Blueprints
Best Practices + Technologies = Working ML Blueprints

What is QuickStart ML Blueprints

and what it is not?

QuickStart ML Blueprints is a set of complete blueprints for solving typical machine learning problems. It leverages best-in-class open source technologies and materializes best practices for structuring and developing machine learning solutions.

QuickStart ML Blueprints is not another black-box "ML Platform"
For who?
QuickStart ML Blueprints an open-source project designed to help data scientists and machine learning engineers streamline their workflow with reusable building blocks, best practices, and guidelines
We want to make data scientists' and machine learning engineers' job more efficient. The framework provides reusable building blocks, best practices, and guidelines that can be used to streamline any data science workflow. It's designed for experts who want to work smarter, not harder.
QuickStart ML Blueprints is developed by data science consultants, who use it to improve their own workflow and make their commercial projects smoother, consistent, and efficient. The framework is released as open source to share the experience with the community, and the developers welcome feedback and contributions from other experts.
Conceptions and Benefits
Decreasing Time-to-Market by enabling faster PoCs
The idea behind QuickStart ML Blueprints is to provide the opportunity to prototype ML solutions more efficiently using well-proven tooling and keeping the highest quality and maintainability of the code. The benefits could be summarized shortly as:

Sharing best practices for developing ML products
Organizing and standarizing Way-of-Work for data scientists
To materialize ML development best practices as concrete working examples we use a modern technology stack. Our main assumption is to stick to the state of the art, well-proven open source tooling. We want the QuickStart ML Blueprints to be applicable and adjustable to any MLOps architecture, so we avoid using commercial or proprietary software for essential functionalities.
Way of work with QuickStart ML Blueprints
The QuickStart ML Blueprints is basically a collection of examples written in Python, structured with Kedro and backed up with robust open source tooling. You are free to take from it as much as you need. The typical procedure that allows to harness all its advantages is:
Use Kedro starter
Start a new project using QuickStart ML Blueprints Kedro Starter
Reduce dataset size
Sample your data to reduce its size and speed up initial prototyping
Study blueprints
Study examples from QuickStart ML Blueprints repository and decide what is to be taken as is for your solution and what should be adjusted or developed from scratch
Build environment locally
Build a containerized working environment on your local machine and add packages you need to get started
Iterate to build local prototypes
Iterate, iterate, iterate - modify environment, build pipelines, document your work, write tests, track experiments until your solution architecture looks OK
Go full-scale
Transfer your work along with the environment into the cloud and run it on full-scale infrastructure and on complete data. Develop your codebase further if needed and when the results are satisfying, enjoy having a production-grade ML system that is ready to be deployed
ML Blueprints
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