Model Development

Your business needs a model that performs a useful task: making a prediction, ranking recommendations, classifying information, or supporting a decision. Choosing an algorithm is one part of that work. You also need evidence that the model performs well enough for the way it will be used.

bixod develops, trains, and evaluates custom machine learning models. We can build a new model, improve an existing one, or adapt a suitable pretrained model when that fits the task. The starting point is your use case, available data, and definition of success.

When this service helps

• You have a defined problem and data, but need expertise to develop and train a model.
• An existing model produces too many errors or struggles with important groups of users or types of input.
• A prototype looks promising, but you need a rigorous evaluation before investing in deployment.

A trained model and evidence for the next decision

We agree on the modeling task, evaluation criteria, and deliverables before development. The work can include:

• A baseline and modeling approach. A reference point for performance and a reasoned choice of algorithms or architectures suited to the problem.
• Training and refinement. Feature development, training workflows, tuning, and experiments to compare candidate models.
• An evaluation you can examine. Results on held-out data, analysis of important failure cases, and clear explanations of limitations and tradeoffs.
• Reproducible model assets. Model artifacts, training code, configuration, and documentation that support future development and retraining.
• Integration requirements. Input and output definitions, resource needs, and the conditions the application must handle when using the model.

Measure what matters to the application

A single accuracy score can hide consequential errors. We select evaluation measures around the task, including the costs of different mistakes, coverage of relevant cases, and constraints such as response time. We also consider how the model compares with the current process or a simpler approach.

Our data science leadership has developed recommendation systems and natural language processing applications in prior roles. That experience informs how we connect model behavior to practical product requirements.

If your data needs preparation, we can include data curation. When the model is ready, solution automation connects it to your applications and operating workflows.

Build a model around your business problem

Tell us what you want to predict, recommend, or classify, what data you have, and how the result would be used. If you already have a model, share where its performance falls short.