We engineer production-grade MLOps platforms, transforming data science experiments into reproducible, monitored, and continuously improving ML systems that deliver business value reliably at scale.
Most ML projects die in staging. Models trained in notebooks lack the reproducibility, monitoring, and retraining infrastructure needed to stay accurate in production. When data distributions shift, model performance silently degrades, often without anyone noticing until business outcomes deteriorate.
We build MLOps platforms that bring software engineering discipline to machine learning, including automated training pipelines, versioned feature stores, model registries with staged promotion, and real-time drift monitoring that triggers automatic retraining when performance drops below defined thresholds.
Key differentiator: We define "done" for ML as: model in production with monitoring, retraining, A/B test framework, and documented rollback procedure. A deployed endpoint alone is not sufficient.
End-to-end MLOps capabilities from experiment tracking through production monitoring and continuous retraining.
MLflow 2.x for experiment tracking with automatic parameter logging, metric curves, artifact storage, and dataset versioning. Weights & Biases for rich sweep visualizations and team collaboration on hyperparameter tuning runs. Neptune.ai for large-scale experiment comparison across 500+ runs. Every experiment records: Git commit, data version, environment snapshot, and system metrics.
Feast for open-source feature serving with online (Redis) and offline (Iceberg) stores, eliminating train/serve skew by sharing feature computation logic. Tecton for managed enterprise feature platforms with streaming feature pipelines. SageMaker Feature Store for AWS-native deployments. Feature lineage tracking ensures every model version maps to exact feature set versions used in training.
Kubeflow Pipelines for Kubernetes-native ML workflow orchestration with component caching, conditional branching, and parallel training runs. Vertex AI Pipelines for managed GCP deployments with auto-scaling training jobs. AWS SageMaker Pipelines for integrated data processing, training, evaluation, and conditional registration gates. All pipelines version-controlled and triggered by data drift events or schedule.
MLflow Model Registry with staged promotion workflow: Staging → Production → Archived. Each registered model version links to: experiment run ID, training dataset hash, evaluation metrics, feature store snapshot, and Docker image digest. Automated evaluation gates reject model promotion if performance metrics fall below baseline thresholds. Full audit trail for regulatory compliance.
Seldon Core on Kubernetes for advanced deployment patterns: A/B testing, shadow mode, multi-armed bandit routing, and canary releases. KServe for serverless model serving with auto-scaling to zero. BentoML for packaging models with dependencies into portable OCI images. NVIDIA Triton Inference Server for GPU-accelerated batch and streaming inference with dynamic batching and concurrent model execution.
Evidently AI for comprehensive model health reports: data drift (statistical tests: KS, PSI, Wasserstein), prediction drift, and target drift with HTML dashboards. Arize for real-time performance monitoring with embedding drift detection for NLP/vision models. WhyLabs for data profiling and anomaly alerting. Automated retraining triggered via Kubeflow or SageMaker Pipelines when drift thresholds are exceeded.
We assess your current MLOps maturity against the Google MLOps maturity model (Level 0 to Level 2) and design a realistic progression plan. Most organizations start at Level 0 (manual, notebook-driven) and target Level 1 (automated training pipeline) within 3 months.
Our MLOps engineers work embedded with your data science team, building platform capabilities without disrupting active model development and ensuring adoption through collaboration, not mandates.
Audit current model development and deployment processes across five dimensions: experiment tracking, data versioning, pipeline automation, model serving, and monitoring. Score against the Google MLOps maturity model. Identify the highest-impact models to prioritize for MLOps infrastructure investment. Deliver a phased implementation roadmap with team training recommendations.
Deploy MLflow or Weights & Biases as the experiment tracking server. Instrument existing notebooks with automatic parameter and metric logging. Implement DVC for dataset versioning with remote storage backend (S3/GCS). Establish reproducibility standard: any experiment in the registry must be re-runnable from scratch with identical results. Time-box: 3 weeks.
Deploy Feast or Tecton feature store. Migrate top-10 features from ad-hoc preprocessing scripts to shared feature definitions. Build automated training pipelines with Kubeflow or SageMaker Pipelines for the two to three highest-value models. Pipelines include: data validation (Great Expectations), model evaluation against production baseline, and conditional MLflow registry promotion.
Integrate model training pipelines with Git-based CI/CD (GitHub Actions or GitLab CI). Every pull request triggers: data validation, unit tests for feature transforms, training pipeline execution on sample data, and evaluation against champion model. Configure MLflow Model Registry promotion gates with approval workflows. Deploy canary release infrastructure with Seldon Core or KServe.
Deploy Evidently AI dashboards for data drift and prediction drift monitoring on all production models. Configure Arize for embedding drift detection on NLP models. Set drift alert thresholds and wire automated retraining triggers back to training pipelines. Implement shadow mode evaluation, where the new model challenger runs alongside the champion and accumulates evidence before promotion. Deliver monthly ML health reports.
MLOps platforms transforming ML from science projects into reliable, business-critical systems.
Built a production MLOps platform for a fintech company's real-time transaction fraud model. XGBoost model trained on 200M transactions, served via KServe at 2ms P99 latency. Evidently AI detects feature drift on 80+ input features daily. When drift exceeds PSI threshold of 0.25, automated Kubeflow retraining pipeline triggers without human intervention. Model performance has remained above 94% AUC for 18 months continuously.
94% AUC maintained, 12× faster deploymentsImplemented a HIPAA-compliant MLOps pipeline for a radiology AI model classifying chest X-rays. All training data versioned with DVC on AWS GovCloud S3. MLflow registry tracks full provenance chain: annotation dataset version → training run → model artifact → serving endpoint. FDA 510(k) submission supported by complete MLflow audit trail and Evidently AI drift reports demonstrating performance stability.
FDA audit-ready model provenance trailDesigned a scalable MLOps platform for a legal tech company's contract clause classification service covering 47 categories across 200K+ contract types. Hugging Face transformers fine-tuned with automated hyperparameter sweeps via Weights & Biases. A/B testing with Seldon Core routes 10% traffic to challenger model before full promotion. Embedding drift monitored with Arize, triggering retraining when new contract types emerge in the distribution.
89% clause classification accuracy at scaleMigrated a retailer's demand forecasting from weekly Excel-based processes to a fully automated MLOps platform. Feast feature store serves 120+ features (historical sales, promotions, holidays, weather) to both training jobs and real-time serving. Prophet + LightGBM ensemble trained on 5-year SKU history. SageMaker Pipelines retrain 8,000+ SKU models weekly. WhyLabs monitors prediction distributions to catch distribution shifts from supply chain disruptions.
31% reduction in inventory overstock costsStart with an MLOps Maturity Assessment: we audit your current ML workflows, score your maturity level, and deliver a prioritized platform roadmap in 2 weeks.