AI Model Accuracy Troubleshooting

My supervised-learning application is already live, but its accuracy is lagging behind expectations. I need an experienced AI architect or engineer who can jump on a series of quick screen-share calls with me, inspect the current pipeline, and guide me toward immediate, measurable improvements.

You’ll be diving into:
• Dataset quality and preprocessing steps (feature selection, class balance, train/validation split)
• Model architecture and hyper-parameter tuning techniques (grid/random search, cross-validation)
• Evaluation metrics now in use versus metrics that might expose hidden issues
• Practical ways to harden the model before deployment (regularization, ensemble ideas, monitoring hooks)

Acceptance criteria
• Accuracy uplift demonstrated on our held-out validation set
• Clear, step-by-step notes or code snippets that document every change we make together
• All work completed through live calls and follow-up messages so I can replicate the fixes independently

I’m ready to start ASAP and will be scheduling the first call as soon as we agree on scope and time.