How much data do we need?

It depends on the problem. Some predictive models work well with a few thousand historical records, while computer vision may need hundreds to thousands of labeled images. We assess this early.

What is MLOps and why does it matter?

MLOps applies DevOps practices to machine learning, covering versioning, automated training, deployment and monitoring. It ensures models work reliably in production and can be updated safely.

Can AI models explain their decisions?

Yes. We use interpretable models where appropriate and explainability techniques like SHAP to show which factors drive each prediction.

Who owns the models and data?

You do. All code, models, and data remain your intellectual property, deployed in your cloud environment.

What is the difference between AI/ML and generative AI?

Traditional ML predicts or classifies based on patterns in structured data. Generative AI creates content such as text, summaries or code using large language models. Many solutions combine both.

Cloud, Data & AI

AI & Machine Learning Solutions

Q: How do we know if AI is right for our problem?

We start with a discovery phase to assess the business value, data availability and feasibility. If a simpler rules-based or analytics solution is better, we will tell you.

Practical machine learning that solves real business problems and runs reliably in production.

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What you get

Use cases ranked by value and feasibility
Proof of concept in weeks, not quarters
MLOps for reliable production models
Explainable, monitored and retrainable

Overview

Why AI & Machine Learning with VN Softverg

AI pays off when it is pointed at a specific, measurable problem: forecasting demand, flagging fraud, predicting churn, inspecting products or routing documents. We start by identifying the use cases with the clearest value and the data to support them, then prove feasibility quickly before scaling.

Our ML engineers and data scientists work with Python, scikit-learn, XGBoost, PyTorch and TensorFlow, and deploy on platforms like Amazon SageMaker, Azure Machine Learning, Vertex AI and Databricks. We build features, train and evaluate models with honest metrics, and explain how they reach their predictions.

Most ML projects fail in the move from notebook to production. That is why MLOps is built in, with experiment tracking, model registries, CI/CD for models, monitoring for drift and retraining pipelines, so models keep performing after launch.

Capabilities

Everything you need, under one roof

From strategy to day-two support — the full scope of our ai & machine learning work.

AI Use-Case Discovery

Workshops and data assessments to identify, size and prioritize AI opportunities, with clear success metrics before any model is built.

Use-case backlog and scoring
Data readiness assessment
Success metrics and business case

Predictive Analytics

Forecasting, churn prediction, lead scoring, risk scoring and anomaly detection using gradient boosting, time-series and deep learning models.

Feature engineering pipeline
Trained and evaluated models
Model performance report

Computer Vision

Image classification, object detection, OCR and visual inspection with PyTorch, OpenCV and YOLO-family models, deployed in the cloud or at the edge.

Labeled dataset strategy
Vision model
Inference API or edge deployment

Natural Language Processing

Text classification, entity extraction, sentiment analysis and document understanding using transformer models from Hugging Face and cloud AI services.

NLP pipeline
Evaluation dataset and results
Integration with business workflow

MLOps & Model Deployment

Experiment tracking, model registry, automated training and deployment pipelines with MLflow, SageMaker, Azure ML or Vertex AI.

MLOps pipeline
Model registry and versioning
Deployment and rollback process

Model Monitoring & Responsible AI

Monitoring for data and prediction drift, bias checks, explainability with SHAP and scheduled retraining to keep models accurate and fair.

Drift and performance dashboards
Explainability reports
Retraining schedule

Our commitments

Measurable from day one

4–6 wks

Proof of concept

Typical timeline to validate a use case on your data with agreed success metrics.

100%

Models monitored

Target for production models covered by drift and performance monitoring.

Defined

Success metrics upfront

Every project starts with agreed business and model metrics before development.

< 200 ms

Inference latency target

Typical goal for real-time prediction APIs, tuned per use case.

How we deliver

A clear path from kickoff to results

Discover

Identify use cases, define success metrics and assess data availability and quality.

Prototype

Explore data, engineer features and build baseline models to prove feasibility.

Validate

Evaluate against holdout data and business metrics, and review results with stakeholders.

Productionize

Deploy models with APIs or batch pipelines, CI/CD and integration into business systems.

Monitor & Improve

Track performance and drift, retrain on schedule and expand to new use cases.

Tools & platforms

The right stack for the job

Languages & Libraries

Pythonscikit-learnXGBoostLightGBMpandasNumPy

Deep Learning

PyTorchTensorFlowKerasHugging Face TransformersOpenCVUltralytics YOLO

ML Platforms

Amazon SageMakerAzure Machine LearningGoogle Vertex AIDatabricks ML

MLOps

MLflowKubeflowWeights & BiasesEvidently AISHAPDockerFastAPI

Business benefits

Better forecasts and earlier warning of risks
Automation of repetitive, judgment-based tasks
Faster path from idea to validated proof of concept
Models that stay accurate through monitoring and retraining
Transparent, explainable decisions for stakeholders and regulators

Estimate my project

Who it's for

Built for teams like yours

Demand Forecasting

Retail, manufacturing and distribution businesses planning inventory and staffing.

Fraud & Anomaly Detection

Financial services and e-commerce teams flagging suspicious transactions in real time.

Customer Churn Prediction

Subscription businesses identifying at-risk customers early for retention campaigns.

Visual Quality Inspection

Manufacturers detecting defects on production lines using computer vision.

Document Classification

Healthcare, insurance and back-office teams routing and extracting data from documents.

Predictive Maintenance

Industrial and IoT operations predicting equipment failures from sensor data.

FAQ

Questions, answered

Something else on your mind? Ask our team →