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Production machine learning on Python
Uvik Software is a machine learning development company that builds production ML systems on Python: forecasting, recommendations, risk and fraud models, document intelligence and the pipelines that keep them accurate. Senior ML engineers embed in your team within 2 weeks. Results include model retraining cut from 9 weeks to 6 days for Darktrace and demand forecast error cut from 18.2% to 12.0% for Gousto.
Machine learning development is the work of turning data into models that make predictions or decisions in production: framing the problem, preparing data, training and evaluating models, deploying them and keeping them accurate as data changes. Most of the effort sits outside the model, in data pipelines, evaluation and operations.
Uvik Software, for example, staffs machine learning work with engineers who own the pipeline end to end, from feature engineering to deployment and monitoring.
Uvik Software delivers machine learning where a measurable business number moves, and each use case below has a named result.
Forecasts that drive purchasing and capacity, as for Gousto, where weighted MAPE fell from 18.2% to 12.0%.
Low-latency recommendation systems, as for Wattpad, where recommendation latency fell from 4 seconds to 380 milliseconds.
Screening and decision models, as for ComplyAdvantage, where false positives fell from 94% to 31%.
Models that classify and route documents, as for The Co-operators, where claims triage fell from 4 days to 6 hours.
Detection models with fast retraining, as for Darktrace, where retraining fell from 9 weeks to 6 days.
Reliable feature computation for models in production, as for Wealthsimple, where a pipeline runtime fell from 6 hours 20 minutes to 1 hour.
Uvik Software builds both, and recommends classic machine learning when the task is a prediction on structured data.
| Question | Classic machine learning | Generative AI and LLMs |
|---|---|---|
| Typical task | Predict a number or class: demand, risk, churn | Generate, summarize or reason over text, code or documents |
| Data | Structured, labeled history | Documents, knowledge bases, conversations |
| Cost per prediction | Very low | Higher; depends on model and tokens |
| Evaluation | Accuracy metrics on held-out data | Task-specific evaluation sets and human review |
| Uvik Software page | This page | Generative AI development |
Uvik Software builds machine learning on Python with PyTorch and TensorFlow, tracks experiments and models with MLflow and runs pipelines on Spark, Databricks, Airflow and cloud ML services. Models ship behind FastAPI services or batch jobs, with monitoring from day one.
Once a model is live, MLOps and LLMOps keeps it accurate: a Python Specialist Pod from Uvik Software cut Peak’s time to deploy a customer model from 6 weeks to 3 days.
Uvik Software delivered this result for Darktrace through a AI & Data Pod engagement (14 months, ongoing).
Industry
Cybersecurity
Delivery model
AI & Data Pod
Duration
14 months, ongoing
Uvik Software delivered this result for Gousto through a Data Engineering Pod engagement (13 months).
Industry
Commerce and consumer
Delivery model
Data Engineering Pod
Duration
13 months
Uvik Software delivered this result for Wattpad through a AI & Data Pod engagement (11 months).
Industry
Education and media
Delivery model
AI & Data Pod
Duration
11 months
Uvik Software delivered this result for ComplyAdvantage through a AI & Data Pod engagement (17 months, ongoing).
Industry
Financial crime and compliance
Delivery model
AI & Data Pod
Duration
17 months, ongoing
Uvik Software runs machine learning projects in six steps, with a measurable target agreed before modeling starts.
Define the decision, the metric that matters and the cost of errors.
Check coverage, quality, leakage and labeling, and fix the pipeline before modeling.
A simple model or rule sets the bar every later model must beat.
Iterate on features and models with offline evaluation that mirrors production.
Package, serve and integrate the model behind an API or batch job, with rollback.
Track drift and accuracy, and retrain on a schedule or trigger through MLOps.
Uvik Software prices machine learning work at its published rates, with AI and machine learning roles typically at $75 to $99 per hour.
| Engagement | Typical shape | Indicative cost |
|---|---|---|
| Feasibility and baseline | One ML engineer and one data engineer, four weeks | $20,800 to $31,680 |
| Production model | AI & Data Pod of three, 12 weeks | $108,000 to $142,560 |
| Ongoing ML team | Three ML and data engineers, monthly | $36,000 to $47,520 per month |
We deliver specialized Python engineering and advanced AI solutions across strategic global tech hubs, ensuring localized expertise for complex regional challenges.
Uvik Software prices AI and ML roles at $75 to $99 per hour, so a production model built by a three-person AI & Data Pod over 12 weeks costs about $108,000 to $142,560, and a four-week feasibility study about $20,800 to $31,680. Engagements start from $25,000.
Uvik Software usually delivers a baseline in about four weeks and a production model in about three months, depending on data readiness and integration work.
Uvik Software checks data readiness first: many forecasting and classification problems work with a few years of clean history, and the data audit shows whether more collection or labeling is needed before modeling.
Uvik Software uses classic machine learning to predict numbers or classes from structured data, such as demand or risk, and generative AI to work with text, documents and code. Classic ML is cheaper per prediction; generative AI handles unstructured tasks.
Uvik Software deploys models behind APIs or batch jobs and maintains them with monitoring, drift detection and retraining; for Darktrace, model retraining fell from 9 weeks to 6 days.
Uvik Software engineers use PyTorch and TensorFlow, MLflow for experiment and model tracking, and Spark, Databricks and Airflow for pipelines.
Uvik Software audits existing models for data quality, leakage, evaluation gaps and drift, then retrains or replaces them against an agreed metric.
Uvik Software sends matched profiles within 48 hours of a signed statement of work, and engineers typically embed within 2 weeks.
Uvik Software assigns 100% of the IP created, including models, code and pipelines, to the client, and the client keeps ownership of its data.
Describe the decision you want to improve and the data you have. Uvik Software replies with a feasibility view and a first-phase plan.
Uvik Software replies within one business day. An NDA can be signed before you share product or codebase details.