Hire ML developers to build and scale your software.

There is no single rate for ML developers. Cost depends on the seniority you need, the engagement model you choose (adding developers to your team, a dedicated team, or a fixed-price project), how long the engagement runs and the scope of the work. For ML work, the biggest drivers are how much data cleaning and labeling is needed before modeling can start, whether predictions are served in real time or in batches, and how much monitoring and automated retraining the model needs to stay accurate. Share your requirements and we will come back with a quote for your situation.

You tell us the technology, seniority, role and team structure you need. We recommend ML developer profiles that match those requirements, and you review the profiles and speak with the engineers yourself before anyone joins your team. When you interview, the areas worth testing are preventing data leakage and choosing a time-based validation split; feature engineering for tabular data and handling missing values; selecting evaluation metrics for imbalanced classes; detecting data and concept drift in production; packaging and serving a model behind an API or as a batch job; and experiment tracking and model versioning with MLflow or similar.

It depends on which engineers with the right skills are available when you get in touch, so we do not publish a fixed start date. Availability is part of the conversation when we recommend profiles, and the start date is agreed with you before the engagement begins.

Because you interview the engineers before anyone joins, fit is tested before work starts. If concerns come up once the engagement is running, raise them with us directly. How changes to the team are handled is set out in the terms you agree with us at the start.

A freelancer suits small, well-defined tasks you can manage yourself. A dedicated developer joins your team under your direction. A dedicated team adds several roles around your product, and a project-based team delivers a defined outcome end to end.

Check three things first: whether you have enough labeled historical examples of the outcome you want to predict, whether the same inputs will be available at prediction time, and whether the data is consistent across sources. A short data audit and a simple baseline model answer these questions faster than a full build.

For tabular business data such as transactions, customer records and sales history, gradient-boosted trees like XGBoost or LightGBM usually match or beat neural networks with less data, faster training and easier explanation. Deep learning earns its complexity on images, audio, free text and very large datasets, where it learns features that are hard to engineer by hand.

Its accuracy usually drifts as customer behavior, prices or upstream data sources change. Production models need monitoring of input distributions and prediction quality, alerts when either moves beyond a threshold, and a repeatable retraining pipeline with versioned data and models, so a refreshed model can be validated and rolled out without manual rework.

Versioning data, code and models together, tracking experiments in a tool such as MLflow, automating training and evaluation in a pipeline, and deploying through the same review process as application code. The aim is that anyone on the team can reproduce a model and see exactly which data and parameters produced it.

Batch scoring, run on a schedule and written to a database, is simpler and cheaper and suits forecasts, churn scores and nightly recommendations. Real-time serving behind an API is needed when predictions depend on what a user is doing right now, such as fraud checks at checkout, and demands attention to latency and scaling.

Pick a metric tied to the business decision, such as precision at a fixed alert volume for fraud or forecast error on key products, and compare it with a simple baseline. Validate on data from a later period than the training data, so the result reflects how the model will perform on future cases.

Yes. Models can train directly on data from warehouses such as BigQuery, Snowflake or Redshift, and feature logic is often best expressed in SQL alongside your existing transformations. Sharing feature definitions between training and serving avoids a common failure where the live model receives inputs calculated differently from the ones it learned on.