Numeric results with bulletproof confidence: this is what companies actually sell when promoting their machine learning product. Yet this seems out of reach when the product is both generic and complex, with much of the inner calculations hidden from the end user.
So how can code improvements or changes in core component performance be tested at scale? Implementing API and Load Tests is time-consuming, but thorough: defining parameters, building infrastructure and debugging. The bugs may be real, but they can also be a result of poor infrastructure implementation (who is testing the testers?).
In this session we will discuss how Airflow can help scale up testing in a stable and sustainable way.
