Service / ML
Machine & Deep Learning
from notebook to production.
Machine learning development services—classification, forecasting, computer vision, and NLP models trained on your data and deployed where your team already works.
- 01
Advanced algorithms
Modern ML/DL for complex, high-dimensional problems.
- 02
Precision models
Tuned on your dataset with clear evaluation metrics.
- 03
Production ready
Batch or real-time inference with monitoring.
- 04
Continuous learning
Retraining playbooks as new data arrives.
Specializations
Supervised learning
Classification and regression for labeled data.
- Forecasting
- Fraud models
- Segmentation
Unsupervised learning
Clustering and anomaly detection.
- Customer clusters
- Outlier alerts
- Basket analysis
Deep learning
Neural nets for vision, speech, and language.
- Computer vision
- NLP
- Speech
Reinforcement learning
Systems that learn from feedback loops.
- Optimization
- Autonomous agents
- Pricing
Delivery steps
Data prep
Collect, clean, and label.
Feature engineering
Signals that improve model quality.
Training
Baseline through production candidate.
Evaluation
Holdout tests and error analysis.
Deployment
API, batch, or edge with monitoring.
Use cases
- Predictive maintenance
- Churn prediction
- Image & video analysis
- NLP pipelines
- Recommendations
- Fraud detection
- Supply chain optimization
- Quality control
Stack
TensorFlow · PyTorch · Scikit-learn · XGBoost · OpenCV · Hugging Face · MLflow
FAQ
Questions clients ask before they outsource.
- 01How much do machine learning development services cost?
- A baseline model with evaluation typically starts around $5,000 USD; production deployments with monitoring and retraining pipelines range $15,000–$60,000+ depending on data complexity. All projects are fixed-quote with milestones.
- 02What if our data isn't ready for machine learning?
- That is common. The first phase of every engagement is a data audit—we assess quality and volume, define a labeling strategy if needed, and tell you honestly whether ML is the right tool before you commit to a build.
- 03How do you deploy and maintain models in production?
- Batch or real-time inference behind an API, containerized with Docker, with drift monitoring and retraining playbooks handed off to your team. We deploy into your cloud (AWS, GCP, Azure) or manage hosting for you.
- 04Do you work with overseas teams and time zones?
- Yes—most clients are in the US, UK, UAE, Canada, and Australia. We schedule weekly demos in your time zone and keep all specs, metrics, and documentation in shared English-language docs.
