A startup spent three months building a churn prediction model—0.94 AUC on validation—only to see accuracy drop 20% in production due to feature drift. Sound familiar? Most ML projects sink between Jupyter notebooks and production, wasting up to 70% of time on futile experiments. We build systems that work for years: MLOps from the first commit and reproducibility guaranteed. Our engineers are AWS and GCP certified, and over 10+ years we've delivered 50+ projects to production. Beyond classic tasks (classification, regression, forecasting), we actively work with LLMs: custom RAG pipelines and fine-tuning models on your data. For custom ML development covering classification, regression, forecasting, and LLM/RAG with fine-tuning, we apply MLOps for reliable ML deployment.
Why standard ML models fail in production
The issue isn't the algorithms—XGBoost, transformers, or LLMs are all capable. Key causes:
- Data leakage during cross-validation—time series require temporal splits, not random. Cross-validation (time series) is the correct approach.
- Lack of monitoring—feature drift goes unnoticed until revenue is lost.
- Uncalibrated probabilities—critical for threshold-based tasks like fraud detection.
We address these during EDA: check for target leakage, build validation schemes robust to temporal shifts.
How we build production ML systems
Our process is not a black box. Let's break it down with a binary classification example for online retail.
- Data Analysis—explore distributions, correlations, missing values, and outliers. Use SHAP to uncover non-obvious dependencies.
- Feature Engineering—temporal aggregates, lag features, one-hot encoding with cardinality limits. For text: TF-IDF or BERT embeddings.
- Model Selection—XGBoost, LightGBM, CatBoost, logistic regression with Optuna. Cross-validation with time-based blocking.
- Calibration—Platt Scaling or Isotonic Regression. AUC-ROC may not change, but probabilities become interpretable.
- MLOps Pipeline—MLflow for experiments, Git versioning of data (DVC), CI/CD for model promotion. Drift monitoring with Evidently AI.
The result: not just a model, but an API service ready for up to 10,000 RPS.
How we guarantee model quality in production
Quality isn't only about development. We implement monitoring for data drift and metrics, automatic alerts, and regular retraining. For instance, MLflow simplifies experiment reproduction threefold compared to manual logging. We provide a maintenance guideline and an SLA for incident resolution within 24 hours.
What's included in the final result
After signing the contract:
- Documentation (Model Card, Data Card, API spec)
- Inference service (Docker + FastAPI + tests)
- Training for your team (2–3 workshops)
- Access to infrastructure (MLflow, Git, CI/CD)
- 3 months of post-deployment support
Timelines: prototype from 2 weeks, production from 2 months. Project estimation is free and takes 2 days. Typical investment for a production ML system starts at $50,000, with potential cost savings of over $200,000 annually by automating decision processes.
Approach comparison
| Criteria | Notebook | Production ML system |
|---|---|---|
| Reproducibility | Low (manual run) | High (containerization, pipelines) |
| Monitoring | None | Data drift, metrics, alerts |
| Scaling | Impossible | Horizontal (Kubernetes) |
| Time to production | 2 weeks (prototype) | 2–4 months (full cycle) |
Project complexity and timelines
| Task type | Example | Prototype time | Production time |
|---|---|---|---|
| Classification/regression | Churn prediction | 2-3 weeks | 2-3 months |
| NLP (text) | Review classification | 3-4 weeks | 3-4 months |
| LLM/RAG | Knowledge base chatbot | 4-6 weeks | 3-5 months |
| Computer Vision | Defect detection | 4-8 weeks | 4-6 months |
Typical mistakes when ordering ML
- Skipping EDA—80% of production problems originate at this stage.
- Ignoring MLOps—a model without monitoring is more dangerous than no model.
- Unrealistic expectations: 0.99 AUC on real data is rare. We honestly show the achievable metric ceiling.
Our custom ML development and machine learning services are designed to deliver bespoke ML models that perform reliably in production. We combine production ML expertise with MLOps to ensure your investment yields measurable returns. For classification, regression, forecasting, and LLM/RAG tasks, our process is three times faster than traditional approaches due to automated pipelines. Contact us for a free consultation and project estimate.
Our experience: 10+ years in ML, 50+ projects in production, AWS and GCP machine learning certifications. Official scikit-learn documentation confirms our cross-validation approaches. Contact us for a project assessment—we don't sell templates, we design solutions for your data.