Honestly man, with a four month deadline, you should probably stay away from anything experimental like Mojo or Julia. They are cool but the ecosystem just isnt there yet for a high-stakes commercial project. Stick with Python because thats where all the support is... if you hit a wall, you can find an answer in seconds. Python is fast enough because it is basically just a wrapper for C++ anyway when you are using the big libraries. I would suggest being very careful about trying to optimize too early. You might want to consider these safety tips:

• Stick to Python but use NVIDIA TensorRT 8.6 Optimizer for your inference. It squeezes a ton of speed out of your models without needing to rewrite everything in a lower-level language.
• Make sure you have the right hardware from the start so you dont hit a ceiling. We used an NVIDIA Jetson AGX Orin 64GB Developer Kit for a similar factory floor project and it handled real-time vision perfectly. As for the math, you dont need to be a professor, but definitely brush up on your linear algebra. If you dont understand how tensors actually work, you are gonna spend days just trying to get your data shapes to align. Ngl, debugging dimension errors is where most of the time sinks are. Play it safe and stay with the tried-and-true stack so you dont get stuck in a hole three weeks before the deadline.

> Is Python actually fast enough for production-level inference or am I gonna regret not starting with something lower level? The secret is that Python is basically just a management layer. All the heavy math happens in C++ or CUDA under the hood anyway. For your production inference, you can usually stick with Python for the logic and use something like NVIDIA TensorRT SDK or ONNX Runtime to handle the execution. It optimizes the model graph so it runs way faster than raw Python ever could. Since you're on a 4-month crunch, dont waste time on Rust or C++ for the whole build. Just use Python for training and then export your models. If the real-time vision part gets heavy, look into Luxonis OAK-D Pro PoE Camera because it handles the AI processing right on the hardware, which saves your backend from choking. It basically offloads the vision pipeline so your main CPU doesnt have to sweat it. About the math... honestly, you just need to understand linear algebra basics like matrix dimensions and dot products. If you can visualize how data flows through a layer, you're fine. You dont need to manually derive backpropagation anymore. Library docs are usually enough to get by once you understand the shapes of your data. Given your Java background, you might find Deep Java Library DJL interesting for the deployment phase if you want to keep the production environment in a language you already know well. It lets you run engines like PyTorch or TensorFlow within a JVM environment. Its pretty solid for enterprise stuff where you need stability.

• Im satisfied using Oracle GraalVM Community Edition for Java-based speed.
• The NVIDIA RTX A5000 24GB GDDR6 works well for real-time vision with no complaints.