Machine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
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Division
Service area
Machine Learning, NLP & Computer Vision
Engagement
Project · Team · Managed
Overview
Machine learning remains the right tool for many high-value problems: predicting churn, forecasting demand, scoring credit or fraud risk, recommending products, optimising prices. We build models that are accurate, explainable where decisions affect people, and operable — with reproducible training pipelines, a model registry, automated deployment and monitoring for drift and performance decay.
Common use cases
- Demand forecastingInventory and capacity planning from historical and external signals.
- Risk and fraud scoringReal-time scores for transactions, applications or accounts.
- RecommendationPersonalised product, content or next-best-action recommendations.
- Churn predictionEarly identification of at-risk customers with reason codes.
Quick answers
Machine Learning Development at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is machine learning development?
- Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
- Who is it for?
- Typically product companies adding AI features, enterprises automating knowledge work, and teams whose AI pilot needs to become a dependable production system.
- What does Shivacha provide?
- Problem framing
- Feature engineering
- Model development
- Explainability
- MLOps
- Monitoring
- Which technologies are used?
- Python, MLOps, Computer Vision, Natural Language Processing, Speech AI, AI Inference & Serving — chosen to fit your stack and constraints.
- How does the process work?
- Frame the prediction → Audit the data → Baseline then improve → Deploy with MLOps → Monitor and retrain.
- What affects the cost?
- Number and quality of data sources
- Accuracy targets and evaluation effort
- Integrations with business systems
- Model choice, hosting and per-request cost
- Human-in-the-loop and audit requirements
- Data residency and privacy constraints
- How long does it take?
- A scoped proof of value usually takes 4–8 weeks; a production system with evaluation, integrations and guardrails typically takes 3–6 months.
- How do I get started?
- Share a short brief in the form below, book a 30-minute call or message us on WhatsApp. A senior engineer replies within one business day; NDA on request.
Capabilities
What we deliver
Problem framing
Target definition, success metrics and decision integration.
Feature engineering
Robust features from transactional, behavioural and external data.
Model development
Baselines through gradient boosting and deep learning as needed.
Explainability
Reason codes and feature attribution for decisions.
MLOps
Pipelines, registry, CI/CD for models and reproducibility.
Monitoring
Drift, data quality and performance alerts in production.
Architecture
Engineered right from day one
The layers we typically design for machine learning, NLP & computer vision, adapted to your stack and partners.
- ExplainabilityFeature importance and reason codes where decisions affect customers.
- Data leakageRigorous validation splits so offline accuracy reflects production reality.
- Latency budgetsModel size and serving architecture matched to real-time requirements.
- FairnessBias testing across relevant segments for decisions with customer impact.
Delivery
How an engagement runs
- 1
Frame the prediction
Define the target, decision it supports, acceptable error and business metric.
- 2
Audit the data
Assess availability, quality, leakage and labeling needs before modeling.
- 3
Baseline then improve
Start with simple, explainable baselines and add complexity only when it pays.
- 4
Deploy with MLOps
Versioned models, reproducible pipelines and automated deployment.
- 5
Monitor and retrain
Track drift and performance in production and retrain on a schedule or trigger.
Security
Security built into delivery
Controls we apply by default on this kind of work — not a separate phase at the end.
Data boundaries
Permission-aware retrieval so users only see answers from documents they may access.
Guardrails
Input and output checks, tool allow-lists and human approval for consequential actions.
No training on your data
Provider settings and contracts chosen so your data is not used to train third-party models.
Audit trail
Prompts, sources, tool calls and approvals logged for review.
Related services
Often combined with
NLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreComputer Vision Development
Computer vision systems for inspection, detection, recognition and visual analytics — from cloud APIs to edge devices.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreDedicated team
Machine Learning Team
ML engineers and data scientists for predictive models and MLOps.
Work & insights
Related thinking
Agentic claims intake with human approval
A reference design for an AI workflow that reads claim submissions, extracts and validates data, and prepares cases for adjusters.
Learn morePermission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreFAQ
Frequently asked questions
Do you use deep learning or simpler models?
Whatever performs best for the problem and constraints. For tabular business data, gradient-boosted models are often strongest and more explainable; deep learning suits images, text, audio and very large datasets.
Can models make real-time decisions?
Yes. We deploy models as low-latency services with feature stores for real-time features where needed.
How much data do we need?
It depends on the problem. Some tasks work with a few thousand labeled examples, especially with pre-trained models; others need much more. A short data audit gives a reliable answer before significant investment.
Can models run on devices or at the edge?
Yes. We optimise and quantise models for mobile, embedded and edge hardware where latency, connectivity or privacy require local inference.
Next step
Build Your AI Product.
Tell us about your machine learning development requirements — goals, timeline and constraints. We will reply with questions, an approach and next steps.
- Senior engineer reads every enquiry
- Reply within one business day
- NDA on request
Your details are used only to reply to this enquiry.