Machine learning models, data pipelines, and predictive analytics built for production.
Search all agencies with AIWhat buyers ask most before hiring a ML & Data Science partner — answered in plain terms.
A machine learning and data science agency builds predictive models — demand forecasting, churn prediction, fraud detection, recommendation engines, and pricing optimization. They handle the full ML pipeline: data cleaning, feature engineering, model training, validation, deployment, and ongoing monitoring and retraining.
Machine learning development typically costs $25,000 to $150,000+ depending on data availability, model complexity, and integration requirements. Simpler predictive models (churn, demand forecasting) run $25,000–$50,000. Custom neural networks and large-scale recommendation systems run $75,000–$200,000+.
A focused ML model (churn prediction, demand forecasting) takes 8–16 weeks from data assessment to production deployment. Complex ML systems with custom architectures take 4–9 months. Ongoing model maintenance and retraining is a recurring operational cost after deployment.
ML agencies work with Python-based frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost), cloud ML platforms (AWS SageMaker, Google Vertex AI, Azure ML), data pipelines (dbt, Airflow, Spark), and MLOps platforms (MLflow, Weights & Biases) for experiment tracking and model governance.
Model drift is the degradation of a deployed ML model's accuracy as real-world data patterns change over time. ML agencies address drift through scheduled retraining pipelines, performance monitoring dashboards, and automated alerts when accuracy falls below defined thresholds. Ongoing model maintenance typically costs $2,000–$8,000 per month.