Phase 01
Audit the data
What you hold, how clean it is, and whether the question can be answered at all. We say so early if it cannot.
Machine Learning
From a data audit to a model in production — wired into the product, not left in a notebook.
- Python
- PyTorch
- scikit-learn
- MLflow
- BigQuery
- Airflow
Outcomes
What you walk away with
- Predictions inside the product
- A pipeline you can trust each week
- Metrics that show lift, not just accuracy
Scope
How we can help
- Forecasting and demand models
- Recommendation and ranking
- Computer vision and language
- Monitoring after go-live
How this one runs
Three phases, and you are in all of them.
Good fit
This is for you if…
- You hold years of data and act on none of it
- Buying, staffing, or pricing is decided on a gut feel
- A notebook proved a model and it never shipped
Questions we get
- How much data do we need?
- Less than most people expect for forecasting, more than most expect for vision. The audit in week one gives you a straight answer before you commit to a build.
- What if the model does not beat the baseline?
- We tell you and stop. A rule that works is a better outcome than a model that impresses.
A quiet next step
Tell us what you want live. We will reply within a day.
A short brief is enough. No form maze.