To Build Successful ML, You Have to Fail Fast and Early
Fueled by data, ML models find patterns and make predictions faster as compared to humans. Victor Thu, President, of Datatron talks about the best practices for ML deployment.
Machine learning projects can’t be approached in the same way as traditional software projects. Speed is of the essence – you need to be able to try things out, fix them and try them again. In other words, you’ve got to be able to fail fast and early.
Not your same old software approach
With a conventional software approach, you’re intentionally programming using decision logic. You want to be as precise as you can and therefore build in logic that allows the software to work. Once the logic of the application is built, there is no need to make any changes other than to fix bugs. It’s a highly methodical process in which you need to ensure each sequential step in the process is correct before moving to the next step – you make incremental progress along the way. It’s a well-established approach that’s long been proven to work for software development.
