docs(machine_learning_patterns.md): add enterprise ml patterns tutorial (#1696) #1704
+422
−0
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
add tutorial covering: data validation with eager/lazy evaluation time series operations and validation feature engineering with backend-agnostic transformations environment management for production/testing closes narwhals-dev#1696
|
thanks a tonne for your pr! will take a look in the week, looks really good |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
What type of PR is this? (check all applicable)
Related issues
Checklist
Description
This PR adds a comprehensive tutorial on enterprise-grade machine learning patterns in Narwhals. The tutorial consolidates backend-agnostic patterns that are essential for production ML workflows.
Patterns Covered
Data Validation Patterns
Time Series Operations
Feature Engineering
Environment Management
Additional Context
The tutorial aims to provide clear, actionable guidance for developers building robust ML pipelines with Narwhals, while maintaining backend independence and performance optimization.