Choosing an ML Partner: 12 Questions

• Start with the business decision the model should improve.
• A good partner checks your data before promising results.
• Every model should beat a simple baseline to be worth it.
• Predictions must reach the people who act on them, with reasons.
• Ask about drift, ownership and running costs before you sign.
• Run a pilot with a clear way to stop if results fall short.

This guide covers twelve questions to ask a machine learning partner, what good answers sound like, red flags and how to compare partners. Put plainly: choose the partner who starts with your decision and your data, not with their algorithms.

Good answer. A specific decision, such as which accounts to call or how much stock to order.

Red flag. "We'll find insights in your data."

Start with the decision, as covered in machine learning for operators.

Good answer. A short data review before any promises.

Red flag. Accuracy claims before seeing your data.

Data quality matters, as covered in data KPIs.

Good answer. A simple rule or average, such as each account's usual order gap, as in wholesaler reorder prediction.

Red flag. No comparison at all.

Good answer. On data the model hasn't seen, and on real results after launch.

Red flag. Only accuracy on the data used to build it.

Good answer. In dashboards, CRM or apps people already use, as weighed in ML vs business intelligence.

Red flag. A separate report nobody opens.

Good answer. The main reasons behind each prediction, in plain language.

Red flag. "The model is too complex to explain."

Good answer. Regular checks across groups where decisions affect people, such as hiring or credit.

Red flag. No plan, or "the data is neutral."

Good answer. Accuracy monitoring and a retraining plan.

Red flag. "Once it's built, it's done."

Good answer. You do, in writing, as covered in owning your code.

Red flag. Models locked inside the partner's platform with no export.

Good answer. Hosting, data updates, monitoring and retraining, estimated up front.

Red flag. Only the build price.

Make the case, as covered in the business case for predictive analytics.

Good answer. Comparable projects, measured outcomes and references.

Red flag. Demos only.

Industry fit matters, as covered in why generic AI fails on specific workflows.

Good answer. One decision, a time limit, clear measures and a way to stop, as covered in AI pilots that fail safely.

Red flag. A large contract before any proof.

For vision projects, see a 90-day computer vision pilot.

Score each answer. Clear, specific and open about limits.

Weigh the baseline and pilot answers most. They reveal realism.

Understand the parts. As covered in the ML stack explained.

Beat the baseline. By a meaningful margin.

Decisions changed. Actions taken because of predictions.

Business results. Revenue, costs or time saved.

Accuracy over time. Watching for drift.

Cost to run. Against the estimate.

Agree on the decision, review the data and set the baseline the model must beat.

Build the model, test on data it hasn't seen and show predictions with reasons where people already work.

Run the pilot, compare against the baseline and decide to expand, adjust or stop.

Astra Results Marketing starts with your decision and your data. We check data before promising results, beat a simple baseline, explain predictions in plain language, plan for drift and keep the model and code in your name.

Engagements begin with a data and decision review through our machine learning and advanced analytics team.

Related reading

• Machine Learning for Operators, Not Data Scientists
• The ML Stack Explained for Non-Engineers
• Making the Business Case for Predictive Analytics

Which business decision the model will improve. Partners who start with your decision, not their tools, deliver results you can use.

A model is only worth its cost if it beats a simple rule or average by a meaningful margin.

Not before reviewing your data. Early promises are a red flag.

You should, in writing, along with the code and data, with the ability to export it.

Hosting, data updates, accuracy monitoring and retraining as conditions change.

With a time-limited pilot on one decision, clear measures and a way to stop if results fall short.

Ready for an ML partner that starts with your decision? Astra Results Marketing reviews your data first, beats a simple baseline, explains predictions in plain language and keeps the model and code in your name. ▸ CALL (786) 321-2866 · ▸ REQUEST YOUR CONSULTATION