As technology is progressing, artificial intelligence (AI) and machine learning (ML) are evolving in new sectors. Within software development, AI and ML drive programmers and testers to be more efficient as well as reaching their goals faster. With AI and ML, testers and developers have access to many capabilities that they didn't have in the past. Therefore, they are able to deliver better and more sophisticated software programs. Over the past few years, AI and ML have taken an important place within software development and we can wonder how much they bring to it.

As interest in Artificial Intelligence (AI), and specifically Machine Learning (ML), grows and more engineers enter this popular field, the lack of de facto standards and frameworks for how work should be done is becoming more apparent. A new focus on optimizing the ML delivery pipeline is starting to gain momentum. Data scientists are becoming more involved in the delivery pipeline of products, and it is a non-trivial task ensuring that their work survives the delivery process. Of course, this isn't a new problem: in the past, traditional software development teams would throw their work "over the wall" to the operations team to serve in production with little to no context. A community effort to solve the inevitable mess resulted in what we now think of as DevOps, removing the wall between development and operations to drive increased efficiency and improve product quality.