Custom AI Model Development

Mapping out the exact problem statement, success metrics, and desired model outcomes based on the client's use case 

Determining the most suitable modeling techniques based on the problem, data, and performance objectives - such as regression, classification, clustering, and deep learning.

Leveraging the client's data to rapidly build model prototypes, validating different modeling approaches. 

Designing optimal model architectures, including neural network layers and parameters, feature engineering steps, and algorithm selection. 

Using the client's computing resources and data, training customized models using techniques like supervised, unsupervised, or reinforcement learning. 

Rigorously evaluating model performance using test data sets and metrics like accuracy, precision, recall, F1 score, and confusion matrices.

Improving model performance through techniques like hyperparameter tuning, loss function changes, and additional data samples.

Package and deploy the model within the client's production IT infrastructure and workflows, with proper versioning and monitoring. 

Setting up ongoing model performance tracking to detect data drift, new training needs etc. to facilitate continuous model improvement.