Tutorial

Cover image generated by Nano Banana Disclaimer: A 50x speedup is not guaranteed. Actual performance depends on the nature of the dataset and the hardware on which the code is run. Please refer to the Benchmarks section below for more information. Introduction Recently, I finally found some time to learn the Rust programming language. I find its memory safety guarantee quite elegant, although it comes with the trade-off of a steep learning curve, especially when it comes to Rust’s ownership and lifetime system. It is very appealing to someone like me, who mainly uses a scripting language and who writes low-level code only from time to time. Writing C/C++ code can easily lead to unstable runtime behavior or unexpected results in such circumstances. ...

Photo Credit Introduction If you are familiar with software engineering, you’d know that automatic testing and continuous integration can save you a lot of debugging time when a project is complex enough and/or involves collaboration between contributors. They help you make sure the new code doesn’t break anything that it’s not supposed to and quickly narrow down the scope of places that could go wrong when failures inevitably happen. For data scientists, we have to test not only against code but also against data to make sure our data pipelines are working correctly. Just like new code can break your software, new data can also break your pipelines. Great Expectations is a tool that protects you from problematic new data: ...