Machine learning is one of the current hot areas. As AI is believed to be the next big breakthrough, machine learning is the technology behind AI that makes it all possible.
However, ML is also a technology, it’s a software algorithm and product that needs to be developed. It’s true that the development of ML systems has become much easier in the last years, since TensorFlow, PyTorch and other frameworks are available for free. So, is the problem of developing ML system solved once we have these frameworks?
No, it’s actually far from that. We still need software engineers to design, implement, deploy and OPERATE these systems in a robust way.
In our research, we studied the adoption of ML in industry in the days before tensorflow, where ML was still perceived to be “advanced statistics” and when deep learning was still called “neural networks” – look at the PDF, and another one here.
Now, if we observe the exponential adoption of ML in industry, we can also catch the big companies to come with mature processes on how to use ML. An example of that is the paper from Microsoft. The paper describes some of the challenges, and, the most important, it describes the workflow of developing ML system. This workflow is focused a lot on data – which is metrics 🙂
What I would like to advocate in this post is that we need to have more statistics and data analysis methods in software engineering education. We should prepare our future software designers to work with data equally as to work with programming!
