This is a common problem in tech: "I've just learned a neat new tool but I never apply it because I can solve all the problems in front of me with the tools I

This is a common problem in tech: "I've just learned a neat new tool but I never apply it because I can solve all the problems in front of me with the tools I have." In effect, you've found a local maximum where every direction seems like a step backwards, or an investment of time without any reasonable payoff. Here are two general strategies to deal with this: 1. Take a well understood, and well documented existing need, and replicate the solution with the new toolkit. Acknowledge from the outset that this will be a step backwards, but go through the details anyway to better understand the technology. The goal isn't to make the system better, but to improve your understanding of ML and it's real world application. By choosing a well understood system, you are only learning applied ML rather than trying to simultaneously learn ML and the problem. Work toward parity with your existing methods. This part is rarely a big step forward, but I guarantee that this process will generate 100 good ideas about where to go next. 2. Find problems that were previously ignored, because they couldn't be solved. Something no one is even thinking to ask for, because none of the prior tools could do the job. This is the ideal situation because you are in a greenfield space where anything is an improvement. For ML specifically look at anywhere a lot of data is being generated but no one has the time to read it all unless something goes wrong. When learning any new technology there is always a gap between learning it in the lab, and trying to execute with it IRL. The best way to maximize your own ability is to simply start applying it and building experience. Don't wait for a perfect halo project.