Machine Learning Consulting Services

Most machine learning projects that stall do not stall on the algorithm. They stall because the same customer exists four times under different identifiers, because nobody agreed what the target variable means, or because there was never a plan for what happens when the model is wrong at two in the morning. We fix the data the model reads, build the model, and put the monitoring around it that keeps it working after we leave.

Machine learning consulting that starts with the data the model reads: entity resolution, labelled targets, a production pipeline, and the monitoring that keeps it accurate.

Machine learning consulting is the work of selecting which predictions are worth making, preparing the data a model can learn from, building and validating the model, deploying it into a system people use, and monitoring it for drift. The majority of the effort is data preparation and deployment rather than model selection.

Machine learning consulting covers use-case selection, data and label preparation, model build and validation, deployment into a production system, and the monitoring that catches drift. Thinklytics scopes the data work first, because most stalled ML projects are blocked on entity resolution and target definitions rather than on the model, and prices the first stage separately so a client can stop if the data is not ready.

Use-case selection against a real decision: what gets predicted, who acts on the prediction, and what changes when they do.

The data work first. Entity resolution, label quality and leakage checks, because a model trained on a broken join learns the break.

A validated model with the error distribution made explicit, including where it is wrong and who that affects.

Deployment into a system people already use, with monitoring, retraining triggers and a documented way to turn it off.

A notebook. A model that only runs on a laptop has not been deployed, and the gap between the two is most of the work.

An accuracy number in isolation. A model at 94 of 100 can still be useless if the 6 are concentrated in your highest value accounts.

A reason to collect more data. Usually the existing data is fine and the labels are not.

A permanent dependency. The deliverable includes the pipeline, the evaluation set and the runbook, so your team can retrain without us.

A scored shortlist of candidate use cases, ranked by value, data readiness and what it would take to act on the output.

A prepared training set with entity resolution, documented labels, and a leakage audit.

The model, with a held-out evaluation and the error profile broken down by the segments that matter commercially.

A production deployment wired into the system of record, with permissions and logging.

Monitoring for drift and data quality, retraining triggers, and a rollback path.

A runbook and handover so your team owns the retraining cycle.

Misrouted claims recovered, and three ML pilots that had been blocked over a year restarted, once member match accuracy went from 75 to 94 of 100.

Suspicious claims identified, with the share needing manual review cut from 18 to 5 of every 100.

Loyalty revenue recovered after a churn model identified 94,000 at-risk members in its first scoring run.

No integration path, no owner, and no agreement on what happens when the model is wrong. Accuracy was never the blocker.

Either leakage in the training set, or no drift monitoring, so nobody noticed the input distribution moved.

The target variable is a business definition dressed as a technical one. It needs an owner before it needs a model.

Identity resolution. If the same entity appears several times under different keys, no amount of modelling recovers it.

Selects which predictions are worth making, prepares the data the model will learn from, builds and validates the model, deploys it into a system people actually use, and sets up the monitoring that catches drift. In practice the data preparation and the deployment are the majority of the work, and the model selection is the smallest part.

Rarely because of the algorithm. The common causes are entity resolution, where the same customer exists several times under different identifiers, label quality, leakage that makes test accuracy meaningless, and no path from a working notebook to a production system with an owner. At Express Scripts three pilots sat blocked for over a year on three separate member ID systems.

What is the difference between machine learning consulting and data science consulting?

In practice the labels overlap. Data science tends to cover analysis and experimentation that answers a question once. Machine learning consulting implies something that runs repeatedly in production and therefore needs deployment, monitoring and retraining. If the deliverable is a report, it is analysis. If it is a system that scores records every day, it is machine learning, and it carries operational obligations.

A scoped use case usually runs six to twelve weeks from data preparation to a monitored production deployment, and the variable is almost always the state of the data rather than the modelling. If entity resolution is needed first, that is its own piece of work and should be costed separately rather than hidden in a modelling estimate.

Usually, and the question is normally the wrong one. The common blocker is label quality and identity resolution rather than row count. A readiness assessment answers it properly by profiling what you have against the specific prediction you want, which is cheaper than collecting data you may not need.

The drivers are the state of the data, whether entity resolution is needed, how many models reach production, and the monitoring and governance the business requires. Thinklytics prices by deliverable rather than by an hours bucket, so scope and cost are agreed before work starts. Request the audit for a fixed quote.

Monitoring for drift and data quality, retraining on a trigger rather than a calendar, and a named owner who can switch it off. A model nobody re-evaluates keeps running long after it stopped being accurate, and that failure is quiet, which makes it the most expensive kind.

Yes. We build on the platform you already run rather than one we resell, whether that is Azure, AWS, Google Cloud, Databricks or Snowflake. A firm that recommends the same stack to every client is describing its own partnership agreement.

Entity resolution and pipelines, which is where most ML projects actually stall.

Demand, inventory and capacity models with the decision attached.