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.
Why Machine Learning Still Wins Where Generative AI Can't
Generative AI gets the headlines. Classical machine learning still runs the bottom line. Forecasting demand for the next quarter, scoring credit risk, detecting fraud at 50,000 transactions per second, predicting equipment failure before it happens, recommending the next best product to a returning customer: these are still ML problems, and an LLM is the wrong tool for any of them. Machine learning development is where measurable business outcomes still get produced fastest, on budgets that finance teams will defend.
At Datasoft Technologies, our machine learning development services span the full lifecycle: predictive ML (regression, classification, time-series forecasting), recommendation systems (collaborative filtering, content-based, hybrid), computer vision (object detection, OCR, defect detection, medical imaging), natural language processing (entity extraction, sentiment, summarization, translation), anomaly and fraud detection, and full MLOps platforms for retraining, deployment, and drift monitoring at production scale.
We engineer with PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, and the data tooling that makes ML actually work in production: Spark, dbt, Airflow, Dagster, Snowflake, Databricks, BigQuery. The hardest part of an ML project is rarely the model. It's the data pipeline, the feature store, the evaluation harness, and the deployment surface. We treat all of those as first-class deliverables.
Maybe you're a startup founder shipping a recommendation engine, an SME building demand forecasting on top of your ERP, or an enterprise standing up an MLOps platform across 50 model deployments with audit trails and drift detection. Whichever it is, we treat machine learning development as production engineering. The best ML model is the one that's still helping the business 18 months from now, not the one that won a Kaggle weekend.
Forecast error on demand, inventory, and capacity prediction
Fraud-detection precision after model + feature engineering pass
From kickoff to a production-grade ML model with full MLOps
Machine Learning Tech Stack
Framework-pragmatic, cloud-flexible. We pick after a one-hour technical scoping based on your data volume, latency requirements, and team skill profile.
ML Frameworks
- PyTorch (research + prod)
- TensorFlow / Keras
- scikit-learn
- XGBoost / LightGBM / CatBoost
- Hugging Face Transformers
- JAX / Flax
Data Engineering
- Apache Spark / PySpark
- dbt
- Airflow / Dagster / Prefect
- Snowflake / Databricks
- BigQuery / Redshift
- Delta Lake / Iceberg
Feature Stores & Serving
- Feast (open-source)
- Tecton
- SageMaker Feature Store
- Vertex Feature Store
- Custom Postgres / Redis
- Real-time + batch parity
MLOps & Deployment
- MLflow
- Weights & Biases
- SageMaker / Vertex AI
- Kubeflow
- BentoML / Modal
- Docker + Kubernetes
Monitoring & Drift
- Evidently AI
- WhyLabs
- Arize AI
- Custom drift detectors
- Feature distribution monitors
- Model performance dashboards
Compute & Cloud
- AWS SageMaker / Bedrock
- Google Vertex AI
- Azure ML
- Modal / Replicate
- On-prem GPU clusters
- NVIDIA Triton Inference Server
Machine Learning Pricing & Engagement
Three engagement models depending on data readiness, scope clarity, and how much of the model lifecycle you want us to own.
| Model | Best For | Typical Range | Timeline |
|---|---|---|---|
| ML POC / Single Model | One predictive ML model: forecasting, classification, or recommendation. Includes data audit, baseline model, and production deployment plan. | $25K to $70K | 8 to 14 weeks |
| ML Platform Build (T&M) | Multiple models, MLOps platform, feature store, drift monitoring, retraining pipelines. Most enterprise ML engagements. | $70K to $300K+ | 3 to 9 months |
| Dedicated ML Team | Long-term ML roadmap, multiple use cases, ongoing operations. Data engineer, ML engineer, MLOps and analyst, embedded. | $15K to $40K / month | 6+ months |
Ranges depend on data volume, model count, MLOps depth, integration scope, and compliance requirements. Written estimate after a 30-minute discovery call.
Machine Learning Outcomes
Every ML engagement is sized against a measurable business outcome agreed in week one. The ranges our clients consistently see are below.
Forecast error
Demand, inventory, and capacity time-series forecasting
Fraud-detection precision
Transaction risk scoring and anomaly detection
Recommendation conversion lift
Hybrid recommendation systems for e-commerce and content
Equipment downtime
Predictive maintenance ML on industrial sensor data
ML Governance, MLOps & Responsible AI
Production ML decays if left alone. We engineer governance and operations in from day one, not after the first incident.
Model Versioning & Lineage
MLflow / W&B model registry, immutable training data lineage, reproducible training runs. Every prediction is traceable to a specific model version and dataset.
Drift Detection
Feature drift, concept drift and performance drift, all monitored continuously with automated alerts and retraining triggers.
Bias & Fairness
Demographic parity tests, equalized odds, disparate impact analysis. Model cards and audit-ready documentation for regulated workloads.
Privacy & Compliance
GDPR, CCPA, India DPDP, HIPAA-aligned ML for healthcare, PCI-aware ML for fintech, differential privacy where appropriate.
Production Reliability
Shadow deployments, A/B tests, canary rollouts, rollback paths. Per-model SLA monitoring and automated failover.
Five ML Mistakes We Help You Avoid
After dozens of ML deployments across regulated and high-stakes domains, the failure modes are predictable. These five kill more ML projects than any algorithmic problem.
Skipping the data audit
Most ML failures start with bad data. We audit data quality, lineage, and label correctness before any model gets trained. It saves months of chasing model errors that were really data errors. The phrase "garbage in, garbage out" is a cliché only because it remains the single most-violated rule in production machine learning.
Optimising the wrong metric
A model with 99% accuracy can still lose money if the 1% errors are the expensive ones. We tie every metric to a business outcome and weight errors by their real cost. Accuracy alone is a vanity metric for any model that touches money or safety.
Ignoring drift
Models decay. Customer behavior changes, distributions shift, label patterns evolve. Without drift detection, you find out via support tickets, which is far too late. We instrument drift monitoring as a non-negotiable production deliverable.
No retraining pipeline
Production ML without an automated retraining path becomes legacy in 6 months. We engineer retraining as part of the initial deliverable, not a follow-up project. Every model we ship has a path from new data to a re-trained, re-evaluated, re-deployed version.
Building a Kaggle solution, not a product
A model that's 0.5% better on a test set isn't worth shipping if it adds 100ms latency or 10x operational cost. We optimise for the production constraint, not the leaderboard. The best ML model is the one your operations team trusts and your finance team can defend.
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. It covers 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 to $40,000. A production-ready ML model with full pipeline, monitoring and integrations ranges $50,000 to $200,000. Enterprise ML platforms with multi-model orchestration and MLOps run $200,000 to $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 to 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 to 8 weeks. A production-ready ML model with pipeline and monitoring typically takes 3 to 5 months. Enterprise ML platforms run 6 to 12 months in phased waves, with the first model live in 3 months and additional models added every 6 to 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.
Should we use machine learning or generative AI for our problem?
It depends on the problem. Use classical ML for forecasting, fraud detection, recommendations, and anomaly detection on structured/tabular data, where it's cheaper, faster, and more accurate than an LLM. Use generative AI when the input or output is unstructured text, image, or speech: chatbots, document summarization, code generation, image creation. Many production systems use both: classical ML for the prediction, generative AI for the explanation. We'll recommend the right mix after a one-hour data review.
How do you detect and respond to model drift?
Three layers. (1) Feature drift: monitoring input distributions for shift. (2) Prediction drift: watching the output distribution. (3) Performance drift: measuring against ground truth as it arrives. We build automated alerts and retraining triggers via Evidently AI, WhyLabs, or Arize. The retraining pipeline is engineered into the initial deliverable, so when drift hits, retraining happens in hours rather than weeks of incident response.
Can you build computer vision applications?
Yes. We build computer vision systems for object detection (YOLO, DETR), OCR (PaddleOCR, Tesseract, Donut), defect detection in manufacturing, medical imaging (DICOM-aware pipelines), retail shelf analysis, document understanding, video analytics, and autonomous-vehicle perception components. We optimise for the constraint that matters, whether that's edge deployment latency, cloud throughput, or accuracy on rare classes, and we ship with full eval harnesses and confidence-thresholded fallbacks.
How do you ensure ML model fairness and avoid bias?
Fairness is a measurement discipline. We test models for demographic parity, equalized odds, and disparate impact across protected attributes when relevant. We audit training data for sampling bias, label bias, and historical bias. Every production model ships with a model card documenting its training data, evaluation results, and known limitations. For regulated workloads (credit, hiring, healthcare), this isn't optional. It's the difference between deploying and being regulated to a halt.
Machine Learning Pricing
Ranges by model maturity: PoC, production model, or full ML platform. Run the full cost calculator for a scoped estimate.
$10K to $30K
4 to 8 weeks
- Data preparation
- Baseline + 1-2 candidate models
- Eval harness
- Stakeholder report
$30K to $100K
3 to 5 months
- Deployed inference API
- MLflow / W&B tracking
- Drift detection
- A/B testing harness
$100K+
6+ months
- SageMaker / Vertex / Kubeflow
- Multi-model serving
- Feature store
- Self-service for data scientists
Ready to Deploy Intelligence Into Your Business?
Let's build ML models that deliver measurable accuracy improvements and real business value.