Machine Learning Development
StepTo delivers production-grade machine learning — from data pipeline to deployed, monitored model — with a focus on measurable business impact rather than science projects. Our nearshore ML engineers in Belgrade, Serbia work in your timezone (CET) at 40-60% below typical Western European and US agency rates. We are pragmatic about when ML is the right tool, deliberate about MLOps, and transparent about metrics, so the models we build keep earning their keep in production. Prefer to embed specialists directly? See our ML engineer hiring guide.
Machine Learning Services
- Predictive modelling & forecasting — churn, demand, risk, and pricing models trained on your data.
- Recommendation & personalisation — systems that lift engagement and revenue for e-commerce and content products.
- NLP & LLM solutions — classification, extraction, summarisation, and retrieval-augmented assistants built on GPT, Claude, or open-source models.
- Computer vision — detection, classification, and OCR for images and video.
- Data pipelines & MLOps — reproducible training, deployment, drift monitoring, and retraining so models stay accurate over time.
How We Deliver
- Frame the problem — define the business objective, success metrics, and whether ML is genuinely the right approach.
- Data & proof-of-concept — assess and prepare data, then validate feasibility with a scoped POC before larger investment.
- Productionise — build the pipeline, deploy the model behind an API, and add monitoring and evaluation.
- Operate & improve — watch for drift, retrain, and iterate as data and requirements evolve.
Why StepTo
- Cross-industry ML and deep-learning experience, from forecasting to generative AI.
- Pragmatic about when ML beats a simpler solution — we will tell you honestly.
- MLOps and monitoring built in, so models survive contact with production.
- Transparent metrics and explainability for trust and compliance.
- 40-60% cost savings versus Western European and US agencies at equivalent seniority.
FAQ: Machine Learning with StepTo
- How do you decide whether ML is the right solution at all?
- We start by being honest about whether a problem actually needs machine learning. Many problems are solved better and cheaper with deterministic rules, good analytics, or an off-the-shelf API than with a custom model. ML earns its place when there is enough quality data, the patterns are genuinely complex, and the cost of being occasionally wrong is acceptable. We assess your data and the business objective first, and will tell you if a simpler approach wins — that candour saves clients a lot of wasted budget.
- Do you build custom models or use foundation models and LLMs?
- Both, depending on the task. For language tasks — summarisation, classification, extraction, conversational interfaces — we frequently build on foundation models (GPT, Claude, or open-source models) with retrieval-augmented generation and fine-tuning, which is far faster and cheaper than training from scratch. For structured prediction, recommendation, forecasting, and computer vision, a purpose-built model trained on your data is often the right call. We pick the approach that meets your accuracy, latency, cost, and data-privacy constraints.
- What does MLOps mean in practice, and why does it matter?
- MLOps is everything that keeps a model useful after the demo: reproducible training pipelines, versioned data and models, automated evaluation, deployment, monitoring for drift and degradation, and retraining. Most ML projects fail not at the modelling stage but at the operational one — a model that worked in a notebook quietly rots in production as data shifts. We build the pipeline and monitoring so your models stay accurate and you can iterate safely.
- How do you handle data privacy and regulated industries?
- We design ML pipelines for compliance from the start — data minimisation, anonymisation or pseudonymisation, access controls, audit trails, and, where required, on-premise or private-cloud deployment so sensitive data never leaves your environment. We have built compliant pipelines for regulated sectors and align with GDPR and sector-specific frameworks such as HIPAA.
- How do you make model decisions explainable?
- For any model that affects people or business-critical decisions, we treat explainability and clear metrics as part of the deliverable. We report honest accuracy, precision/recall, and error analysis rather than a single flattering number, and use techniques like feature importance and SHAP values so stakeholders understand why a model behaves as it does. That transparency is essential for trust and for meeting regulatory expectations.
- What does ML development with StepTo cost?
- Because our ML engineers are based in Serbia, you get senior expertise at 40-60% below comparable Western European and US agency rates. A scoped proof-of-concept is usually a fixed engagement to validate feasibility before larger investment, and ongoing ML work is commonly delivered through a dedicated team from $15K-25K/month for three engineers. If you would rather embed individual specialists, see our ML engineer hiring guide.