Machine Learning Solutions
Production-ready ML models that drive intelligence into every layer of your business
Machine Learning That Moves from Lab to Production
Datasoft Technologies builds and deploys machine learning models that solve real business problems at production scale. Unlike theoretical ML experiments, our solutions are engineered for reliability, explainability, and continuous improvement in live environments.
Our team of PhD-level data scientists and ML engineers combines deep theoretical knowledge with practical engineering expertise, using frameworks like TensorFlow, PyTorch, scikit-learn, and XGBoost to build models that deliver measurable accuracy improvements.
We implement full MLOps pipelines that automate model training, validation, deployment, and monitoring, ensuring your ML systems remain accurate and performant as data distributions shift over time.
ML Models Deployed
Accuracy Rate
Processing Speed
Inference
Our ML Solutions
Comprehensive machine learning services across all major ML paradigms
Supervised Learning
Classification and regression models for churn prediction, fraud detection, price forecasting, and quality control.
Unsupervised Learning
Customer segmentation, topic modeling, dimensionality reduction, and pattern discovery in unlabeled datasets.
Deep Learning
Neural network architectures for computer vision, NLP, speech recognition, and complex pattern recognition tasks.
Time Series Forecasting
Demand forecasting, sales prediction, capacity planning, and financial time series modeling with LSTM and Prophet.
Anomaly Detection
Real-time detection of outliers, fraud, equipment failures, and network intrusions in streaming and batch data.
MLOps & Model Deployment
End-to-end MLOps pipelines with automated retraining, A/B testing, model registry, and drift monitoring.
Why Choose Our ML Team
PhD-Level Data Scientists
Deep theoretical knowledge combined with practical engineering expertise for novel problem solving.
Production-Ready Models
Models built from the start for production — not just notebooks — with proper testing and monitoring.
Explainable AI
SHAP values and LIME explanations making model decisions interpretable for business stakeholders.
Continuous Improvement
Automated retraining pipelines and drift detection keep models accurate as your data evolves.
Our ML Development Process
Data Collection
Data audit, collection, cleaning, and feature engineering for ML readiness.
Model Training
Experiment tracking, hyperparameter tuning, and model selection.
Evaluation
Business-aligned metrics, bias testing, and explainability analysis.
Deployment
Containerized model serving with A/B testing and canary rollouts.
Monitoring
Data drift detection, performance monitoring, and automated retraining.
Machine Learning Development FAQs
What is machine learning development?
Machine learning development is the end-to-end process of building, deploying and operating ML models that turn data into predictions and decisions — covering problem framing, data collection and labeling, feature engineering, model training, evaluation, deployment, monitoring and continuous improvement (MLOps).
How much does machine learning development cost in 2026?
A focused ML proof-of-concept on existing data typically costs $15,000–$40,000. A production-ready ML model with full pipeline, monitoring and integrations ranges $50,000–$200,000. Enterprise ML platforms with multi-model orchestration and MLOps run $200,000–$800,000+.
Supervised vs unsupervised vs deep learning — which to use?
Use supervised learning when you have labeled data and want predictions (classification, regression, forecasting). Use unsupervised when you want to find patterns (clustering, anomaly detection). Use deep learning when you have lots of data and complex inputs (images, audio, language). We'll pick based on your data and the business question.
Do you handle data labeling and preparation?
Yes. We handle the full data pipeline — data collection, cleaning, deduplication, labeling (in-house or via services like Scale AI / Labelbox), augmentation, feature engineering and train/val/test splits. Data prep typically consumes 60–70% of total ML project effort and is the biggest determinant of model quality.
How long does an ML project take to deliver?
A focused ML proof-of-concept ships in 4–8 weeks. A production-ready ML model with pipeline and monitoring typically takes 3–5 months. Enterprise ML platforms run 6–12 months in phased waves — first model live in 3 months, additional models added every 6–8 weeks afterward.
Do you handle MLOps and model monitoring?
Yes. We set up MLOps with model versioning (MLflow, DVC), experiment tracking, automated training pipelines (Kubeflow, Vertex AI, SageMaker), deployment (BentoML, Triton), drift detection, A/B testing and continuous monitoring — so models keep delivering value as data and the world change.
Ready to Deploy Intelligence Into Your Business?
Let's build ML models that deliver measurable accuracy improvements and real business value.