Scaling machine learning programs is very different to scaling traditional software because they have to be adapted to fit any new problem you approach. As the data you’re using changes (whether because you’re attacking a new problem or simply because time has passed), you will likely need to build and train new models. This takes human input and supervision. The degree of supervision varies, and that is critical to understanding the scalability challenge.
- Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML
- The data privacy Cold War is here. Which side are you on?