Model Deployment Jobs

I need an AI-powered, camera-based system that reliably counts every vehicle entering or leaving a parking lot. The model must recognise cars, motorcycles and trucks with at least 90 % accuracy from live or recorded video. I expect a lightweight pipeline—OpenCV for video handling paired with a deep-learning detector (YOLOv8, TensorFlow, or similar) is fine as long as it meets the accuracy goal and can run in real time on a mid-range GPU or an edge device such as Jetson Xavier. Key requirements • Environment: single or multiple fixed cameras covering a parking-lot entrance/exit. • Classes: Cars, Motorcycles, Trucks (separate tallies for each). • Accuracy: sustained 90-100 % counting precision, validated over a representative test set I will provide. • Robu...

I already work with Honeywell APC Profit Suite and Uniformance Process Studio at an intermediate level, but I want to close the remaining gaps through focused, screen-sharing sessions. The goal is to master every step of the workflow on linear models, moving from deep parameter understanding to full model deployment. Here is the flow I’d like us to follow: • URT mastery – walk me through every parameter, what it represents in practice, and how changes influence controller behaviour. • Model tuning – set limits, optimise weights, and test performance until the response is sound and stable. • Model expansion – demonstrate, then let me practise, adding new CVs, MVs and DVs to an existing model without disrupting what is already in service. •...

I have a curated dataset of patients’ genetic profiles and need a deep-learning solution that can reliably flag the presence of heart disease. Because the data are entirely genomic, the job begins with thoughtful preprocessing and feature engineering (handling high-dimensional SNPs, normalisation, train/validation split, class-imbalance techniques if required). My single, overriding success metric is F1 Score; accuracy alone will not do, so the model must be tuned to balance precision and recall. You may choose the exact framework—PyTorch or TensorFlow/Keras are both fine—as long as the final code is clean, reproducible, and GPU-ready. Deliverables • End-to-end Python code or notebook that loads the raw genetic data, performs preprocessing, trains the deep neur...

I have a curated dataset of patients’ genetic profiles and need a deep-learning solution that can reliably flag the presence of heart disease. Because the data are entirely genomic, the job begins with thoughtful preprocessing and feature engineering (handling high-dimensional SNPs, normalisation, train/validation split, class-imbalance techniques if required). My single, overriding success metric is F1 Score; accuracy alone will not do, so the model must be tuned to balance precision and recall. You may choose the exact framework—PyTorch or TensorFlow/Keras are both fine—as long as the final code is clean, reproducible, and GPU-ready. Deliverables • End-to-end Python code or notebook that loads the raw genetic data, performs preprocessing, trains the deep neur...