Machine learning that ships — and explains itself.
I am a machine learning engineer who builds models for production, not for presentations: trained on your data, validated on data they have never seen, explained in plain language, and deployed as software your business can call every day.
Where AI projects usually stall
01Demos that never reach production
Notebook experiments impress in a meeting and die before a real user touches them. Production ML is a different discipline: data pipelines, validation, deployment, monitoring.
02Predictions nobody trusts
A model without validation or explanations is a black box your team quietly ignores. Trust is earned with evidence — held-out scores and reasons for every prediction.
03Manual visual and document work
People inspecting images or reading documents one by one, at human speed and human error rates. Vision and NLP systems do the first pass; people handle the judgement calls.
04Generic tools that do not fit
Off-the-shelf AI does not know your products, your customers or your documents. Custom models are trained on exactly the data your decisions depend on.
What you receive
✓Custom ML models
Classification, regression and forecasting models — XGBoost and scikit-learn — trained on your data for your specific decision.
✓Computer vision
YOLOv8 detection and segmentation pipelines: quality inspection, monitoring and document image analysis with reviewable outputs.
✓NLP & document intelligence
Extraction, classification and retrieval-based assistants over your documents, using Groq and Gemini APIs where language models fit.
✓Evaluation & explainability
Baselines, held-out validation, error analysis and SHAP explanations — so you know what the model knows and where it is blind.
✓Deployment as software
FastAPI services and Streamlit applications, with scheduled retraining where the data demands it — the model as infrastructure.
✓Honest documentation
What the system does, how it was validated, where it is weak, and what would improve it next. No inflated claims, ever.
Tools & technologies
Python & scikit-learn
The foundation: data pipelines, classical models and rigorous evaluation.
XGBoost
Gradient boosting that performs reliably on real, messy tabular business data.
YOLOv8
State-of-the-art object detection and segmentation for vision pipelines.
SHAP
Explanations for every prediction — the difference between a model and a trusted tool.
Groq & Gemini APIs, FAISS
Fast language models and vector search for document intelligence and RAG systems.
FastAPI & Streamlit
How models reach users: as APIs other systems call, or applications people open.
Engineering, not experiments
Problem framing
We define the decision the model supports and the metric that matters commercially — before any algorithm is chosen.
Baseline first
The simple approach sets the bar. The model must beat it clearly on held-out data, or I will tell you the simple approach is the right answer.
Build & validate
Training, cross-validation, error analysis and SHAP explanations — with limitations documented as carefully as strengths.
Deploy & monitor
Served through an API or application, with a retraining path as new data arrives and performance worth watching.
Straight answers
A general chatbot is broad but unmeasured. A custom model is trained on your data for one specific, measurable job — predicting, classifying, detecting — and its accuracy is proven on data it has never seen. The two often work together: language models for text, custom models for decisions.
By testing it on held-out data it never trained on, comparing it against a simple baseline, analysing where it makes mistakes, and explaining its decisions with SHAP. The limits are documented, not hidden.
Yes. I build detection and segmentation pipelines with YOLOv8 — for image and document analysis tasks like quality inspection and monitoring — with outputs a human can review and correct.
Yes. Models are served through a documented API or embedded in a dashboard or application, so your existing systems call the model instead of your team working around it.
A custom prediction model package starts at $1,490 and typically runs two to four weeks. Computer vision and larger systems are scoped individually — always in writing, with deliverables fixed before work begins.
Related services
Predictive Analytics & Forecasting →
Churn, demand and risk models — the commercial face of machine learning.
AI Chatbot Development →
Assistants trained on your business that answer customers around the clock.
API Development & Integrations →
Serve models to any system through clean, documented APIs.
Let's build the model your data deserves
Book a free 30-minute call. Describe the decision you want to improve — I will tell you honestly whether machine learning is the right tool for it.
Book a Free Call Chat on WhatsApp