ML & Data Science
Model deployment, MLOps, anomaly detection, recommendation systems. From classical ML to foundation-model workflows, with production evaluation, monitoring, and rollback discipline.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Machine Learning That Ships to Production
We take models from notebook to production with evaluation, observability, rollout discipline, and rollback paths.
Typical engagement starts when
- a model concept looks promising, but the team needs a production path with monitoring, rollback, and evaluation before launch
- anomaly detection, ranking, or classification is affecting live workflows and the current heuristics are no longer holding up
- the organization has enough data and product pressure to justify ML, but not enough operational rigor around training and serving yet
- leadership needs to know whether this is truly a model problem, a retrieval problem, or a rules problem before more effort compounds
What We Build
| Capability | What We Deliver |
|---|---|
| Anomaly detection | Isolation Forest, autoencoders, and hybrid ML/FM systems for real-time threat detection |
| Recommendation engines | Collaborative filtering and content-based systems with online learning |
| MLOps pipelines | MLflow experiment tracking, model registry, and automated deployment |
| Foundation model fine-tuning | LoRA, QLoRA, and full fine-tuning for domain-specific performance |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Detecting insider threats, fraud, or anomalies in streaming data | Isolation Forest + foundation model reasoning (healthcare pattern) |
| Recommending products, content, or actions from user behavior | Collaborative filtering + online learning pipeline |
| Need domain-specific LLM performance beyond base model capabilities | LoRA / QLoRA fine-tuning with evaluation benchmarks |
| Models in production but no visibility into drift or degradation | MLflow + Prometheus observability + automated retraining triggers |
| Classifying documents, images, or text across multiple languages | Multi-language NLP pipeline (StanfordNLP + custom extractors) |
| Still deciding between ML and a rules-based system | AI Strategy Advisory: assess data readiness first |
Engineering Standards
| Standard | What It Protects |
|---|---|
| Model versioning and experiment tracking | Every production behavior can be tied back to a training run and artifact |
| Rollout and comparison infrastructure | Model changes can be evaluated before broad exposure |
| Drift review triggers | Retraining decisions are based on observed degradation, not calendar habit |
| Production monitoring | Quality, latency, and serving behavior stay visible after launch |
These controls matter because ML systems usually fail at the operational layer first: no clear rollback, no drift visibility, and no agreement on when a model should stop serving production traffic.
Common failure patterns we fix
- teams fine-tuning or retraining models before proving the data, labeling, or evaluation setup is strong enough
- promising notebook results with no production path for rollback, observability, or safe rollout
- models serving live traffic without drift detection, threshold review, or clear ownership when quality degrades
- ML introduced where retrieval, rules, or product changes would solve the problem more simply
- recommendation or anomaly systems tuned for offline metrics while production feedback loops stay weak or invisible
What you leave with
- an ML architecture matched to the real business signal and production operating constraints
- evaluation, rollout, and monitoring criteria that make model changes governable instead of subjective
- serving, retraining, and rollback paths the internal team can operate without guessing
- a clearer decision on where ML belongs in the system and where deterministic logic should still win
Best Fit
- Team already has enough data volume, signal quality, and operational need to justify production ML
- Use case depends on anomaly detection, ranking, classification, or domain-specific model performance
- Engineering leadership wants experiment tracking, versioning, monitoring, and rollback handled as part of the system
- Model outputs affect live product behavior, risk scoring, or analyst workflows and therefore need production discipline
Specialist Capabilities
| Capability | Focus |
|---|---|
| MLOps Engineering | Model serving, feature stores, experiment tracking, ML CI/CD |
Deployments in this area
Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives
How we built a real-time anomaly detection pipeline processing 2.4M events/day using Kafka, Isolation Forest, and foundation models. False positive rate reduced from 68% to under 20%.
Enterprise Data Governance & Document Classification Platform
We engineered a smart document classification and anomaly detection system for an enterprise client, supporting GDPR readiness workflows through ML-driven categorization of corporate files across multiple languages.
High-Throughput Real-Time Facial Recognition Platform
Distributed facial recognition system processing millions of concurrent video streams with >97% accuracy using FaceNet embeddings, Kafka streaming, and k-NN matching.
AI-Powered Video Interviewing & Candidate Analysis Platform
We built an end-to-end video interviewing platform with real-time speech-to-text transcription, automated resume parsing, and semantic search — enabling recruiters to find key candidate responses in seconds.
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