In an ideal world where we reached 100% test coverage, our error handling was flawless, and all our failures were handled gracefully — in a world where all our systems reached perfection, we wouldn’t be having this discussion. Yet, here we are. Earth, 2020. By the time you read this sentence, somebody’s server failed in production. A moment of silence for the processes we lost.

Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.

Continuous Testing is the process of testing at all stages of software development – one after the another- without any human intervention. Continuous Testing is key to faster delivery of Agile products to the market. Continuous Testing makes it possible to eliminate testing as a bottleneck for faster software development and delivery. But the path to achieving Continuous Testing has its own challenges, most common of which are mentioned below.

Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. As organizations embark on machine learning initiatives to derive value from their data and become more “AI-driven” or “data-driven”, it’s essential to find a faster and simpler way to productionize machine learning projects so that they can make business impact faster.