Machine Learning & AI (pipelines, ML models, automation) Turn data into working AI with governed pipelines, reliable deployments, and monitoring that proves ROI. Turn business data into working AI systems—from data pipelines and model development to governed deployments with ongoing monitoring and retraining. We build production-grade MLOps so models ship safely, learn continuously, and show measurable ROI in dashboards leaders can trust.
START AN ML ASSESSMENT View Certifications Key Benefits Faster Delivery: Paved MLOps and templates
Quality & Fairness: Evaluation + bias checks
Reliable Serving: Latency/error SLOs
Governed & Secure: Lineage, approvals, access controls
Measurable ROI: Exec KPIs in BI dashboards
What We Build End-to-End Pipelines: ingest → validate → transform → feature compute → train → evaluate → approve → deploy → monitor → retrain. Model Services: real-time and batch inference APIs with latency/error SLOs. MLOps Foundations: model registry, feature store, experiment tracking, CI/CD for data & models. Use-Case Accelerators: recommendations, routing/prioritization, classification/extraction, anomaly detection, forecasting. Data & Feature Engineering Sources: operational DBs, logs, files, third-party APIs; schema contracts and CDC for change safety. Quality Gates: freshness, completeness, outliers; PII redaction/masking and role-based access. Features: offline/online parity, point-in-time correctness, reuse via a feature catalog. Model Development & Evaluation Experiments: clear baselines, reproducible runs, hyperparameter tracking. Validation: train/val/test splits, cross-validation, leakage checks, fairness/bias reports. Metrics: precision/recall/ROC for classification; MAE/RMSE/MAPE for regression; business KPIs for value alignment. Guardrails: holdout sets, approval workflow, and sign-off evidence for reviews. Deployment & Serving Patterns: batch scoring, real-time microservices, streaming enrichment. Rollouts: shadow, A/B, and canary with auto-rollback on KPI or SLO breach. Optimization: GPU/CPU right-sizing, quantization/pruning, request batching, caching warmups. Monitoring, Drift & Retraining Live Monitors: data drift, concept drift, latency, error rate, saturation. Post-Deployment QA: slice metrics, fairness checks, and anomaly alerts. Retraining: schedules and trigger-based retraining with reproducible pipelines. Dashboards: model health, ROI, and adoption trends for execs and SREs. Governance & Risk Lineage & Provenance: datasets, code, model artifacts, and approvals captured end-to-end. Access & Secrets: scoped tokens, key rotation, encrypted stores. Evidence: change records and evaluation reports to support procurement/compliance reviews (TX-RAMP/HIPAA/PCI context where applicable). CERTIFICATIONS CYBERSECURITY SERVICES Automation Use Cases (Examples) Document & Form Automation: extract fields, classify, route to workflows. Demand Forecasting & Staffing: time-series models for ordering and scheduling. Anomaly Detection: payments, operations, or infrastructure signals. Next-Best-Action & Recommendations: rules + ML for personalization and efficiency. Delivery Approach Discovery & Feasibility — goals, data audit, KPI targets. Data & Features — contracts, quality gates, feature catalog. Modeling & Evaluation — experiments, fairness, sign-off. Serving & Rollout — APIs/batch, shadow/A-B/canary with rollback. Operate & Improve — monitoring, drift detection, retraining, ROI dashboards. FAQs Q: We don’t have perfect data. Can we still start?
Q: How do you prevent bias or model regressions?
Q: Can you integrate models into our existing apps and workflows?
Q: How do you measure and communicate ROI?
Ready to Put ML Into Production? 
