AI/ML 
Rozwój oprogramowania 
80% of enterprise AI projects fail to deliver business value – and 85% of those failures trace back to data quality, not algorithm choice (RAND Corporation 2025, 2,400+ initiatives; Gartner 2025). 42% of companies abandoned most AI initiatives in 2025, up from 17% the year before (S&P Global / McKinsey 2025). The bottleneck is almost never the model. MIT’s 2025 research is blunt: 80% of the work that moves a pilot into production is data engineering, governance, and workflow integration. The algorithm accounts for 20%. VM.PL builds custom ML systems end-to-end, starting where the failures actually happen: the data layer.
VM.PL develops custom ML algorithms and models across the full problem spectrum: supervised classification and regression, deep learning for computer vision and NLP, time-series forecasting, unsupervised anomaly detection, reinforcement learning, and embedding-based architectures for semantic search and RAG. End-to-end responsibility from data audit through architecture selection, model training, explainability, and production MLOps. GDPR- and CCPA-ready, EU-based legal entity. First model in production in 4-8 weeks for well-prepared data; full production-grade system in 3-6 months.
RAG, fine-tuning, or custom training – choosing the right architecture
Not every AI project requires training a model from scratch. VM.PL selects the architecture that fits the use case and data constraints – without a vendor interest in any particular approach. The right choice depends on three variables: how frequently the knowledge base changes, how domain-specific the vocabulary and task are, and how strict the accuracy and latency requirements are.
| Architecture | When it fits | VM.PL recommendation |
| RAG (Retrieval-Augmented Generation) | Large, frequently updated knowledge base · answers must cite verifiable sources · no retraining when content changes · hallucination control required | Default starting point for LLM-based knowledge assistants · fastest to production · most cost-efficient to maintain |
| Fine-tuning | Company-specific tone and dialogue patterns · domain vocabulary the base model does not know · limited domain data as starting point | When RAG accuracy is insufficient · trained on pseudonymized client data · combined with RAG in most production deployments |
| Custom training (from scratch) | Highly specialized domain · proprietary training data · strict accuracy requirements · safety-critical systems (ISO 26262, IEC 62443) | Manufacturing, automotive, industrial research with proprietary sensor or image datasets where base model performance is insufficient |
| Pretrained base model | Standard tasks without domain specificity · text classification, NER, embedding generation | Fastest path when no domain knowledge is required · VM.PL layers fine-tuning or RAG on top when needed |
51% of enterprise AI deployments use RAG in production; fine-tuning sits at 9% – RAG is the dominant pattern, not an experimental alternative (Menlo Ventures 2024 State of Generative AI in the Enterprise). Enterprise RAG systems with well-tuned retrieval pipelines achieve 85-90% answer accuracy on domain-specific knowledge bases; naive RAG implementations without proper chunking and retrieval optimization typically achieve only 10-40% – implementation quality matters more than approach selection (multiple 2024-25 enterprise RAG benchmarks, including NeoBram.ai and Sphere Partners analyses). Context for RAG value: employees spend 30% of their working time searching for information across disconnected systems (IDC). VM.PL has no proprietary base model or SaaS platform to sell. The architecture recommendation follows the use case and data available, not a product roadmap.
Which ML algorithms and model architectures does VM.PL develop – and when?
The ML architecture follows the problem, not the trend. Gradient-boosted trees (XGBoost, LightGBM, CatBoost) consistently outperform deep learning on structured tabular data across benchmark after benchmark – a finding confirmed through 2022, 2024, and 2025 evaluations. The practical conclusion: careful feature engineering and hyperparameter tuning matter more than model architecture on tabular problems. XGBoost on tabular data frequently outperforms a neural network; a CNN is the right choice for defect detection on image streams. VM.PL selects methodically.
| Learning paradigm / architecture | Typical use case | Algorithms and models (examples) |
| Supervised learning – classification | Fraud detection, quality control, credit risk, failure prediction, churn | XGBoost, LightGBM, Random Forest, SVM, MLP |
| Supervised learning – regression | Demand forecasting, energy consumption, price modelling, remaining useful life | Linear / polynomial regression, Gradient Boosting, neural networks |
| Unsupervised learning | Customer segmentation, anomaly detection without labels, sensor pattern discovery | K-Means, DBSCAN, Isolation Forest, Autoencoder |
| Deep learning – computer vision | Defect detection, optical quality control, object detection, OCR | CNN (ResNet, EfficientNet), YOLO, Semantic Segmentation |
| Deep learning – NLP / LLM | Document analysis, assistants, text classification, summarization, extraction | Transformer, BERT variants, LLaMA-based models, fine-tuned GPT |
| Time series | Predictive maintenance, energy load forecasting, financial forecasting | LSTM, GRU, Temporal Fusion Transformer |
| Reinforcement learning | Resource planning, supply chain optimization, robotics control | PPO, DQN, Actor-Critic methods |
| Embedding-based | Semantic search, RAG retrieval, recommendation systems, anomaly detection via distance | text-embedding-3-large, E5-large, BGE-M3 · pgvector/PostgreSQL 16 |
Data preparation, feature engineering, and ETL pipelines – where most projects actually fail
50-70% of ML project effort goes into data preparation – not model development. That is not an inefficiency in the process; it is the correct allocation. Gartner finds that 85% of AI project failures trace back to data quality, and only 12% of organizations are confident they hold data of sufficient quality for AI. Companies lose an average of $12.9 million annually due to poor data quality, much of it from governance gaps: duplicate records, missing values, and contradictory information across ERP, CRM, and operational systems (Gartner 2024). A clean data foundation is the most underrated engineering decision in ML.
- ETL pipeline engineering. VM.PL builds robust data pipelines that extract from ERP, CRM, IoT, and database sources, transform to ML-ready formats, and load to the feature store. Stack: Python 3.11+ · asyncpg for async database access · pgvector/PostgreSQL 16 as feature store · Azure Data Factory for batch ETL. Schema validation, null handling, and type consistency checks are automated and documented. The pipeline is version-controlled, testable, and rollback-capable.
- Feature engineering. Raw data rarely contains directly usable signals. VM.PL extracts and constructs features that represent the learning problem: time-series aggregations, domain-specific metrics, interaction features. Feature selection eliminates redundant and irrelevant variables using correlation analysis, mutual information scoring, and permutation-based importance to prevent the curse of dimensionality without removing signal.
- Strategies for limited training data. Transfer learning (pretrained models as starting point – reduces data requirements by orders of magnitude). Semi-supervised learning (few labeled + many unlabeled examples). Synthetic data generation (statistical sampling or physics-based simulation for realistic training data without personal information). Data augmentation (rotation, noise, translation, cropping for computer vision datasets). The Discovery Phase determines which strategy fits the available data before any model architecture is committed.
- GDPR- and CCPA-compliant data pipelines. Pseudonymization before training as standard: sensitive attributes are replaced by structure-preserving surrogates. On-premise training option for data that must not leave the client’s infrastructure. DPA (Data Processing Agreement) as a standard component of the Definition Phase.
Model quality: overfitting, cross-validation, and evaluation metrics
A model that achieves excellent results on training data and fails on new data has memorized the training set rather than learned the underlying pattern. Model quality is the result of systematic evaluation discipline, not an outcome of algorithm sophistication.
- Bias-variance tradeoff and overfitting control. Overfitting (model learns noise rather than pattern) is controlled through regularization (L1/L2, Dropout), early stopping, and cross-validation. Underfitting (model too simple) shows up in systematically poor performance on both training and test data. VM.PL analyses learning curves to make the diagnosis before tuning begins – the curve shows whether the problem is data volume, model capacity, or noise.
- Cross-validation. Instead of a single train/test split, VM.PL uses k-Fold cross-validation (typically k = 5 or 10) – the model trains and evaluates on k different data split combinations. Result: stable performance estimates with confidence intervals rather than a single point estimate that may be optimistic. For time-series data: Walk-Forward Validation to prevent data leakage from future periods into training.
- Evaluation metrics by problem type. Classification: Accuracy, Precision, Recall, F1-Score, AUC-ROC – selected based on class imbalance and the asymmetric cost between false positives and false negatives. Regression: MAE, RMSE, MAPE, R². Ranking/recommendation: NDCG, MAP. VM.PL defines the primary metric in the Concept Document, based on the business objective, not technical convenience.
- Hyperparameter optimization. Manual search is not practical at the scale of modern ML models. VM.PL uses Bayesian Optimization via Optuna or Grid-/Random-Search with automatic experiment logging via MLflow. Every experiment is reproducible: hyperparameters, data split, metrics, and model artefacts are versioned. An experiment that cannot be reproduced cannot be debugged.
Deploying ML models to production: MLOps, CI/CD, and deployment strategies
A trained model that is not in production delivers no value. The average time from AI prototype to production is 8 months (BCG AI Maturity Reports 2024-25) – and $7.2 million is the average cost of a single failed enterprise AI project (S&P Global). Getting from notebook to production is where the majority of AI projects that built a working model still fail. VM.PL treats deployment as an engineering problem, not an afterthought.
- ML model as a production service. The ML model is packaged as a Docker container and deployed to Azure Container Apps via Azure Pipelines CI/CD. The FastAPI service layer exposes prediction endpoints that client systems call via REST API. Inference latency, throughput, and error rates are instrumented in Azure Application Insights from day one.
- Champion-Challenger deployment. The current production model (Champion) and a new candidate (Challenger) run simultaneously, with traffic split between them. Statistical significance tests determine which model performs better on live production data before the Challenger takes full traffic. This eliminates the risk of an offline benchmark that looks good but underperforms in production.
- Blue-Green deployment. Two complete production environments (blue = current, green = new). Rollback to the previous model version is a pipeline variable change, not a maintenance window. Used for SLA-critical ML services where zero-downtime deployment is a hard requirement.
- Automated rollback. Azure Pipelines monitors defined performance thresholds after every deployment. If a metric falls below the threshold – accuracy drop, latency spike, elevated error rate – the previous model is automatically reactivated without manual intervention. All model versions are versioned in MLflow and reproducible.
- Vitest 4 for Next.js frontend. Any operator dashboard or reporting interface built on Next.js 16 + shadcn/ui is tested with Vitest 4 (unit + smoke). The ML Python service has its own test suite (pytest), fully decoupled from the frontend testing layer.
Embeddings, vector stores, and semantic search architectures
Embeddings – dense vector representations of text, images, or structured data – are the foundation of modern AI systems. Semantic search, recommendation engines, RAG pipelines, and anomaly detection all depend on embedding quality and retrieval efficiency. A good embedding model on a bad retrieval pipeline produces worse results than a mediocre embedding on a well-designed one.
- Semantic search and RAG architecture. Documents are stored as embedding vectors; queries and documents are projected into the same vector space; nearest neighbours are semantically relevant results. VM.PL builds RAG pipelines with LightRAG (lightrag-hku 1.4.9.11) as retrieval framework and pgvector/PostgreSQL 16 as vector store. Embedding models: OpenAI text-embedding-3-large (via Azure OpenAI with Zero Data Retention) or open-source alternatives (E5-large-v2, BGE-M3) for on-premise deployments. No keyword dependency, language-agnostic retrieval, multilingual out of the box.
- Recommendation systems. Product, content, and user embeddings are trained in a shared vector space – similar items cluster near each other. Recommendations are computed as nearest-neighbour search in the embedding space. More efficient than collaborative filtering on large catalogues and handles cold-start scenarios better by using item feature embeddings.
- Anomaly detection via embedding distance. Normal behaviour occupies a dense region of the embedding space; anomalies appear as outliers. Autoencoders or Contrastive Learning generate embeddings where outliers are identifiable as points with large distance to known patterns. Particularly effective for fraud detection (transaction embeddings) and industrial quality control (product image embeddings).
- Vector store scaling. pgvector/PostgreSQL 16 handles medium-scale collections (up to several million vectors) within the existing database infrastructure – no additional tooling required. For very large vector collections (hundreds of millions+), VM.PL evaluates specialized vector database options based on the specific latency, throughput, and data governance requirements of the deployment.
Explainable AI (XAI) and model interpretability
A model that cannot explain its decisions is not deployable in regulated industries, safety-critical systems, or any context where the decision affects a person’s rights or safety. VM.PL designs explainability in from the Definition Phase – because retrofitting SHAP into a deployed model that was never built for it is the wrong order of operations.
- SHAP values (SHapley Additive exPlanations). For every model decision, SHAP delivers a traceable explanation: which features contributed how much to the output, in which direction, and with what magnitude. Works across model types – gradient boosting, neural networks, deep learning – via TreeSHAP (XGBoost, LightGBM) and KernelSHAP. VM.PL integrates SHAP as a standard Delivery Phase component for regulated and safety-critical deployments.
- LIME (Local Interpretable Model-agnostic Explanations). For individual predictions, LIME fits a locally interpretable model around the prediction point. Useful when the global model explanation is too complex to present to a non-technical stakeholder but local explanation of a specific decision is required.
- Regulatory requirements for XAI. EU AI Act (February 2025) mandates transparency and explainability for high-risk AI systems. GDPR Article 22 requires meaningful information about automated decision-making. US: ECOA and FCRA require adverse action explanations in credit decisions. ISO 26262 (ASIL C/D) and IEC 62443 require documented justification for safety-related AI decisions. VM.PL maps applicable requirements in the Architecture Blueprint before model training begins.
Production monitoring, data drift detection, and re-training
A model that performs well at deployment will degrade over time as the world changes. Data drift – the shift in input feature distributions between training and production – is the most common cause of silent model degradation. VM.PL treats MLOps as the live environment for the model – the part of the project that actually runs the system, not a monitoring tab nobody checks.
- Performance monitoring. Azure Application Insights tracks model prediction metrics, latency, throughput, and error rates continuously. Dashboards visible to both ML engineers and product owners – the model’s health is not invisible to the business.
- Data drift detection. PSI (Population Stability Index) and KS tests (Kolmogorov-Smirnov) monitor feature distributions in production against the training baseline. When a feature distribution shifts beyond the configured threshold, a drift alert triggers – before model accuracy degrades enough to be visible in output metrics.
- Controlled re-training. Re-training is not triggered manually or on a fixed calendar. It runs through the Azure Pipelines CI/CD step when drift or accuracy degradation metrics cross the threshold. Every re-training cycle produces a versioned model artefact in MLflow, runs the test suite, and goes through the Champion-Challenger gate before replacing the production model.
- Model versioning and reproducibility. Every model version – training data snapshot, hyperparameters, evaluation metrics, artefacts – is logged in MLflow. Any previous version is reproducible and deployable. A model update that underperforms can be rolled back to the exact prior version in minutes, not hours.
The 4D methodology for ML algorithm and model development
Discovery validates the data before any algorithm choice is made. Committing to an ML architecture before auditing the data is how the 80% failure rate happens. VM.PL does not select an algorithm in the first conversation.
| Phase | Activities | Deliverable | Duration | Go/No-Go gate |
| Discovery | Data quality audit (completeness, consistency, volume, labeling) · use case validation · feasibility study · architecture pre-selection · GDPR / CCPA data assessment · ROI modelling | Concept Document (Go / No-Go) | 1-2 weeks | Go if: data volume and quality sufficient for use case · labeling coverage adequate · legal basis confirmed · feasibility validated. No architecture commitment before this gate. |
| Definition | Algorithm selection · model architecture · training plan · evaluation metrics (primary metric agreed) · deployment concept · explainability requirements · MLOps monitoring design | Architecture Blueprint | 1-2 weeks | Approved by technical lead and product owner · primary metric agreed · deployment target confirmed |
| Delivery | Feature engineering · ETL pipeline (Python 3.11+ / asyncpg / pgvector) · model training · hyperparameter optimization (Optuna / MLflow) · cross-validation · XAI (SHAP) · FastAPI service layer · Azure Container Apps deployment · Azure Pipelines CI/CD · Vitest 4 for Next.js frontend (if applicable) · Champion-Challenger gate | Production ML system | 4-8+ weeks | Primary metric on holdout set within agreed target · latency and throughput confirmed · XAI deliverable reviewed · rollback tested |
| Direction | Performance monitoring (Azure Application Insights) · drift detection (PSI / KS tests) · controlled re-training via Azure Pipelines · MLflow model versioning · model extension as new features arrive | Ongoing roadmap | Ongoing | Monthly accuracy report · drift alert triggers review within 48h · re-training under version control |
Why engineering teams work with VM.PL for ML algorithm development
VM.PL Software House (founded 2004, Wrocław, Poland) has delivered 475+ projects for US, UK, and Western European clients. 150+ engineers, NPS 71. English-first communication, 6-9 hours ahead of US East Coast, 1-4 hours of daily synchronous overlap.
Where VM.PL differs from algorithm consultancies and ML tool vendors:
- No architecture bias. VM.PL has no proprietary model, SaaS platform, or partner resale incentive. The recommendation that comes out of the Discovery Phase is driven by the data and the problem, not the product roadmap.
- Industry Experience: VM.PL develops custom AI and machine learning solutions for various industries, including manufacturing, automotive, biotechnology, research, and fintech. One example is the development of an AI/ML system that identifies bacterial strains based on Fresnel patterns. The system achieved over 96% identification accuracy and reduced identification time from seven days to less than 24 hours. Reference: AI/ML for bacterial strain identification using Fresnel patterns.
- End-to-end ownership. From data audit and ETL pipeline through architecture, training, XAI, deployment, and MLOps monitoring – one partner for the full ML lifecycle. The data engineer, the model developer, and the deployment engineer work from the same context throughout – not from documentation written to bridge gaps between teams that never spoke.
- Production-first engineering. Every architecture decision is made with deployment constraints in mind: inference latency, data governance requirements, integration with existing systems. A model that works in a notebook but cannot deploy is a prototype. It is not billable, and it is not the goal.
- Full client ownership. Source code, model weights, training pipelines, and MLflow experiment logs belong to the client. No ongoing licensing. The system can be transferred to an internal ML team or a different vendor without rebuilding.
- GDPR and CCPA by design. Pseudonymization, on-premise training option, DPA, and documented legal basis are standard contract components. VM.PL is an EU legal entity; GDPR compliance is native.