Synthetic Data Platform: Turnkey Development & Integration

Real data is often inaccessible due to strict regulations in medicine and finance, high labeling costs, or scarcity of rare scenarios. For example, an insurance company needed to generate 1 million synthetic policies while preserving correlations between age and risk. After platform deployment, the time for testing new models dropped from two weeks to two days. We integrate and customize data generation platforms that generate artificial samples retaining the statistical properties of the original while containing no confidential information. Our track record: over five years in MLOps and generative models, more than 50 implemented solutions.

Artificial data solves three key challenges: privacy compliance (GDPR, HIPAA without process changes), rare class expansion (dataset augmentation for Computer Vision or NLP), and system testing under load (generating millions of records with controlled distribution). Reference: General Data Protection Regulation (GDPR) - Wikipedia. We don't just produce — we verify each sample through statistical tests and ML Utility gap, which in 97% of projects does not exceed 2%.

Why synthetic data instead of real?

Generated data provides a controlled distribution unattainable with real samples. In insurance, we generated 10% rare losses that were less than 0.1% in the original data — the ML model's F1-score increased by 15%. This is impossible with simple augmentation.

Building a synthetic data platform

A typical solution architecture includes ingestion, generation, validation, and delivery layers. We use a modern stack: FastAPI for API, React for frontend, PostgreSQL for metadata, S3/MinIO for storage, PyTorch and Hugging Face for models.

┌─────────────────────────────────────────────────────────┐
│                   Data Ingestion Layer                    │
│  [Real Data] → [Privacy Scan] → [Statistical Profiling]  │
└─────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────┐
│                  Generation Engine                        │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │ Tabular (GAN)│  │  Text (LLM)  │  │ Image (Diff) │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────┐
│                  Quality Validation                       │
│  [Statistical Fidelity] [Privacy Audit] [ML Utility]    │
└─────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────┐
│                   Delivery Layer                          │
│  [API] → [Data Catalog] → [Access Control] → [Audit]   │
└─────────────────────────────────────────────────────────┘

Tabular data generation

For structured tables we use CTGAN (Conditional Tabular GAN) or Gaussian Copula — the choice depends on dataset size and required speed. CTGAN is 2x slower than Gaussian Copula, but delivers 5-10% higher accuracy on complex datasets.

from sdv.single_table import CTGANSynthesizer, GaussianCopulaSynthesizer
from sdv.metadata import SingleTableMetadata

metadata = SingleTableMetadata()
metadata.detect_from_dataframe(real_df)

# CTGAN — high quality, 500 epochs
ctgan = CTGANSynthesizer(metadata, epochs=500, batch_size=500,
                         generator_dim=(256,256), discriminator_dim=(256,256))
ctgan.fit(real_df)
synthetic_df_ctgan = ctgan.sample(100_000)

# Gaussian Copula — 10x faster, better at preserving correlations
copula = GaussianCopulaSynthesizer(metadata)
copula.fit(real_df)
synthetic_df_copula = copula.sample(100_000)

Related table generation

When data is normalized (patients → diagnoses → prescriptions), we use HMA Synthesizer, which models the relationship hierarchy.

from sdv.multi_table import HMASynthesizer
from sdv.metadata import MultiTableMetadata

metadata = MultiTableMetadata()
metadata.detect_from_dataframes({
    'patients': patients_df, 'diagnoses': diagnoses_df, 'prescriptions': prescriptions_df
})
metadata.add_relationship('patients', 'patient_id', 'diagnoses', 'patient_id')
metadata.add_relationship('patients', 'patient_id', 'prescriptions', 'patient_id')

synthesizer = HMASynthesizer(metadata)
synthesizer.fit({'patients': patients_df, 'diagnoses': diagnoses_df, 'prescriptions': prescriptions_df})
synthetic_data = synthesizer.sample(scale=1.5)

How we evaluate quality and privacy

We don't deliver a black box to the client. Each produced sample undergoes three checks:

• Statistical Fidelity — Column Shapes and Column Pair Trends (score ≥ 0.9)
• Privacy Audit — data privacy membership inference (MI) attack (new row score > 0.9)
• ML Utility — Train on Synthetic, Test on Real (AUC difference < 2%)

Example code for privacy audit (ML utility test TSTR — Train on Synthetic, Test on Real):

from sdmetrics.single_table import NewRowSynthesis

new_row_score = NewRowSynthesis.compute(
    real_data=real_df, synthetic_data=synthetic_df,
    metadata=metadata, numerical_match_tolerance=0.01
)
# Target: score > 0.9 — synthetic data does not reproduce real records

And the ML Utility test shows whether the data is suitable for model training:

model_real = train_classifier(real_train, real_val)
model_syn = train_classifier(synthetic_train, real_val)
print(f"ML Utility gap: {(model_real.auc - model_syn.auc):.4f}")
# Acceptable < 0.02

Use cases for synthetic data platforms

The main scenario is a shortage of data for training or testing. In the banking sector, we replaced 70% of real transactions with synthetic ones for stress testing: p99 latency dropped by 30%, and anomaly coverage doubled. Get a consultation — we'll assess if your case fits.

How to get started

1. Submit your dataset metadata or schema.
2. Receive a demo synthetic dataset for evaluation.
3. Review quality and privacy reports.
4. Deploy the full platform with your data.

Project workflow

What's included in the result

• Full-featured platform with web interface and REST API on FastAPI and React.
• Integration with your Data Catalog and storage systems (S3, PostgreSQL).
• Automatic privacy audit and ML utility report for each dataset.
• API, architecture, and operation documentation.
• Team training (2–3 days).
• 3-month warranty support.

Implementation timeline

A typical project takes 3 to 4 months. A typical project investment ranges from $50,000 to $150,000, depending on complexity. The timeline may vary depending on the number of data types, source, and UI requirements. Contact us for a consultation — we'll prepare a commercial proposal tailored to your specifics and show a demo of the generator on your metadata the next day.

Consultation and commercial proposal

Our turnkey data platform, built with a FastAPI React data platform stack, includes the ML utility test TSTR. If you have questions about architecture or cost — write to us. We'll prepare a commercial proposal tailored to your specifics and demonstrate a generator demo on your metadata.

MLOps: Infrastructure for Training, Deploying, and Monitoring ML Models

The model is trained, metrics — F1 0.94 on validation. Three months later in production, quality drops by 12%. No one knows when — there is no monitoring. It's impossible to retrain quickly — the training script is in a Jupyter notebook of a data scientist who has already left. Data for retraining is collected manually from three disparate systems. About half of the projects come to us with this pain. We build a turnkey MLOps platform: from experiment tracking to automatic deployment and data drift monitoring. We will assess your infrastructure in 1–2 weeks, and in 4–6 weeks you will get a basic MLOps core running in production. Our team has 10+ years of experience in ML infrastructure, over 50 implementations.

How does MLOps infrastructure benefit your ML projects?

Experiment Tracking and Reproducibility

Without tracking, an ML project turns into chaos: it's unclear which checkpoint is better, which hyperparameters were used, which dataset. Reproducing a result a month later is a quest.

Why is experiment tracking the foundation of reproducibility?

MLflow is an open source standard for tracking. It logs parameters, metrics, artifacts (models, graphs), and code. MLflow Model Registry is a centralized model storage with versioning and lifecycle stages (Staging → Production → Archived). Deployment via MLflow Serving or integration with external systems.

Typical initialization in code:

import mlflow

mlflow.set_experiment("fraud-detection-v2")
with mlflow.start_run():
    mlflow.log_params({"learning_rate": 3e-4, "batch_size": 64, "epochs": 10})
    mlflow.log_metric("val_f1", val_f1, step=epoch)
    mlflow.pytorch.log_model(model, "model")

This is the minimum. In production, we add logging of system metrics (GPU utilization, memory), dataset (hash, version), code (git commit hash). Weights & Biases — richer UI, collaboration features, sweep for hyperparameter optimization. MLflow — for on-premise deployment without external dependencies.

DVC (Data Version Control) — versioning of data and models on top of git. Data is stored in S3/GCS/Azure Blob, only metadata (hashes) in git. dvc repro reproduces the entire pipeline from raw data to metrics.

To ensure reproducibility of training, fix random seeds (torch.manual_seed, numpy.random.seed, random.seed) and record them in experiment metadata. Without this, debugging irregular results is painful. Log the dataset version (DVC hash) and git commit — then any experiment can be reproduced down to the byte.

Pipeline Orchestration: Kubeflow, Airflow, Prefect

A pipeline orchestrator becomes necessary when: A 100-line training script in cron is fine for simple tasks. But as soon as you have a multi-step pipeline (data loading → preprocessing → feature engineering → training → validation → deployment if quality above threshold), you need an orchestrator with retry logic, visualization, and alerts.

Kubeflow — Kubernetes-native orchestrator for ML (see Kubeflow). Each step is a Docker container. Supports parallel steps, conditional branches, artifacts between steps. Integrates with Katib (AutoML), KServe (serving), Feast (feature store).

Apache Airflow — more general DAG orchestrator. Wide ecosystem of operators (S3, Spark, DBT, Kubernetes). Easier to deploy if Airflow already exists in the company.

Prefect / Metaflow — less boilerplate. Prefect 2.x with @flow and @task decorators — quick start for small teams.

Typical training pipeline architecture on Kubeflow:

1. Data ingestion component — fetches data from S3/DB, validates schema via Great Expectations
2. Preprocessing component — transformations, normalization, train/val/test split
3. Training component — training on GPU, logging to MLflow
4. Evaluation component — metric calculation, comparison with baseline in Model Registry
5. Conditional deployment — deploy only if new model is better than current by >2% F1

Each component is a separate Docker image. Pipeline is versioned in git. Scheduled run (retraining once a week on new data) or manual.

Model Registry and Lifecycle Management

Model Registry is not just a checkpoint store. It is a centralized system that knows:

• Which model is currently in production (and with what metrics)
• History of all versions with training parameters
• Metadata: dataset, git commit, validation results
• Lifecycle stage: None → Staging → Production → Archived

MLflow Model Registry — standard. For enterprise — Vertex AI Model Registry (GCP), SageMaker Model Registry (AWS), Azure ML Model Registry.

Model promotion through stages: automatically move model to Staging after successful eval, then manual or automatic (during A/B test) promotion to Production. Rollback — switch to previous Production version in seconds.

Serving: From FastAPI to Triton Inference Server

Simple case. FastAPI + PyTorch/ONNX on one server — 80% of production ML deployments are exactly that. Sufficient for most tasks with load up to 100 req/s.

from fastapi import FastAPI
import onnxruntime as ort

app = FastAPI()
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])

@app.post("/predict")
async def predict(request: PredictRequest):
    inputs = preprocess(request.text)
    outputs = session.run(None, {"input_ids": inputs})
    return {"label": postprocess(outputs)}

Triton Inference Server — production standard for high loads (500+ req/s). Dynamic batching, concurrent model execution, model ensemble. Supports TensorRT, ONNX, PyTorch TorchScript, TensorFlow SavedModel.

KServe — Kubernetes-native ML serving with autoscaling, canary deployments, A/B testing out of the box. Scale-to-zero for inactive models — savings on infrastructure up to 40% annually for a project with 10 models.

Monitoring: Data Drift, Model Drift, Infrastructure Metrics

Monitoring — what is usually done last and regretted first. Three levels.

Infrastructure monitoring. Latency (P50/P95/P99), throughput (req/s), error rate (4xx, 5xx), GPU/CPU utilization. Prometheus + Grafana — standard. Alert when P99 latency > threshold or error rate > 1%.

Data drift monitoring. Distribution of input data changes over time. Detect via PSI (Population Stability Index) for numerical features: PSI > 0.2 — strong drift. Chi-squared test for categorical, Kolmogorov-Smirnov test for continuous. Evidently AI — open source library with ready-made drift tests.

Model drift monitoring. If ground truth is delayed (e.g., we know conversion after a week) — monitor real metrics. If not — surrogate metrics: distribution of prediction scores, proportion of confident predictions.

Alerting. Three levels: INFO (minor drift, log it), WARNING (significant, notify team), CRITICAL (quality dropped below threshold — automatic switch to fallback model).

Why is data drift monitoring important?

Without it, you learn about model degradation only from user complaints or ringing SLA. A drift alert allows you to retrain the model in advance, before errors start causing losses. In one of our projects, PSI monitoring detected drift 2 days after a data source change — this saved the campaign.

Feature Store

Feature Store solves the training-serving skew problem. If preprocessing during training and inference is implemented in two different places — divergence is inevitable.

A Feature Store is needed when:

• Several models use the same features
• Features are computed from streaming data (real-time)
• Large team with different people on feature engineering and model training

Feast — open source Feature Store. Offline store (S3 + Parquet) for training, online store (Redis, DynamoDB) for low-latency inference. Feature definitions as code, materialization job syncs offline → online.

Tecton (commercial), Vertex AI Feature Store (GCP), SageMaker Feature Store (AWS) — managed options with less ops overhead.

CI/CD for ML

ML CI/CD is regular CI/CD plus specific ML steps.

ML-specific checks in CI:

• Reproducibility check: run training with a fixed seed, result must match
• Data validation: Great Expectations or Pandera on schema/distribution checks
• Model performance check: automatic eval on holdout, block merge if degradation > threshold
• Latency regression test: inference must meet SLA

GitOps for deployment. Merge to main → CI triggers training → eval → if passes → automatic deployment to Staging → smoke tests → manual promotion to Production or automatic upon successful canary.

Tools: GitHub Actions / GitLab CI for CI, ArgoCD for GitOps deployment on Kubernetes.

What's Included in MLOps Platform Development

We provide a full cycle of work, documentation, and team training.

Total time from audit to full MLOps platform: 3–5 months. Also possible phased launch: basic level (tracking + serving) in 4–6 weeks.

Cost is calculated individually based on data volume, number of models, and infrastructure requirements. Order an MLOps infrastructure audit — get a roadmap in 1–2 weeks. Contact us for a project assessment — we will send a preliminary estimate within 2 business days.

Note: warranty on architectural solutions — 12 months. We provide integration certificates with major cloud providers (AWS, GCP, Azure). During our work, we have not lost a single client after the first implementation — the experience of 50+ successful MLOps projects speaks for itself. Get a consultation on building an MLOps platform today.