Start Your Next ML Project With This Template — Motivation Getting started is often the most challenging part when building ML projects. How should you structure your repository? Which standards should you follow? Will your teammates be able to reproduce the results of your experimentations? Instead of trying to find an ideal repository structure, wouldn’t it be…

Incorporate Domain Knowledge into Your Model with Rule-Based Learning — Motivation You are given a labeled dataset and assigned to predict a new one. What would you do? The first approach that you probably try is to train a machine learning model to find rules for labeling new data. This is convenient, but it is challenging to…

Create a UI for ML Feature Engineering in One Line of Code — Motivation As a data scientist, you might frequently adjust your feature engineering process and tune your machine learning models to get a good result. Instead of digging into your code to change function parameters: …, wouldn’t it be nice if you could change the parameter values from the UI?

A Fast and Cheap Approach to Update an ML Model in Production — Problem with Batch Learning It is common for data practitioners to use batch learning to learn from data. Batch learning is the training of ML models in batch. An ML pipeline with batch learning typically includes: Splitting the data into train and test sets Fitting a model to the train set Computing the…

A Perfect Combo to Quickly Iterate on Your DS Project — Motivation Imagine your data pipeline looks similar to the graph below. The pink box represents a stage, which is an individual data process. Dependencies are the files that a stage depends on, such as parameters, Python scripts, or input data. Now imagine Dependencies 2 changes. …

Make Your Pipelines Easier to Support and Maintain — Motivation In engineering, maintainability is the ease with which a product can be maintained to: Meet new requirements Cope with a changing environment Improve the performance of the product In a data science project, it is essential to build a maintainable pipeline because: The characteristics of data can change frequently Data…

How to Extract Datetime From Text and Data From Datetime — Motivation Datetime features are important for time series and forecasting. However, you don’t always have clean datetime features to work with. Wouldn’t it be nice if you can automatically extract datetime from text and data from datetime? In this article, you will learn 4 tools to do exactly that. datefinder: Automatically Find Dates and Time in a Python String

Create Clean and Robust Tests with Property-Based Testing — Motivation Imagine you are trying to figure out whether the function processing_fn is working properly. You use pytest to test the function with an example. The test passed, but you know that one example is not enough. You need to test the function with more examples to make…