Model Delivery & Sprints
Production ML & Inference APIs
A fast-paced 6–12 week agile build sprint. We engineer custom predictive neural models, optimize low-latency real-time inference APIs, and establish automated validation testbenches running in your production environment.
Everything Needed to Ship Machine Learning
Every Build Sprint delivers tested, evaluated, and high-performance predictive systems directly into your cloud infrastructure.
Custom Model Training & Tuning
Supervised, unsupervised, and deep neural architectures (PyTorch, XGBoost, LightGBM) with automated hyperparameter optimization.
- Hyperparameter Tuning
- PyTorch / XGBoost Mesh
High-Throughput Inference APIs
Sub-20ms low latency REST and gRPC API endpoints with batching, quantization (ONNX / TensorRT), and connection pooling.
- Sub-20ms Latency
- ONNX Runtime Quantization
Validation & Drift Testbenches
Rigorous K-fold cross-validation, out-of-distribution evaluation, bias checks, and automated regression test harnesses.
- K-Fold Cross-Validation
- Bias & Fairness Testing
Kubernetes Rollout & APM
Production deployment on AWS EKS, GCP GKE, or Azure AKS with auto-scaling GPU pods, OpenTelemetry metrics, and CI/CD pipelines.
- Auto-Scaling GPU Pods
- 24/7 APM Telemetry
How We Deliver ML in 6–12 Weeks
Fast, iterative 2-week sprints delivering testable model candidates directly to staging from Sprint 1.
Feature Engineering & Baselines
Data pipeline setup, feature extraction, baseline model training, and setting target accuracy metrics.
- Feature store ingestion pipeline
- Baseline benchmark evaluation
Algorithm Tuning & Inference APIs
Hyperparameter search, quantization, sub-20ms inference wrapping, and stakeholder demo integration.
- Neural model optimization
- REST / gRPC endpoint staging
Hardening, SRE & Launch
Load testing, drift monitoring instrumentation, canary rollout, and engineering handoff.
- 99.9% Uptime Kubernetes launch
- SRE runbooks & training
Senior Machine Learning Practitioners
Engineers who have deployed high-scale predictive systems to production, not junior experimentalists.
Lead Machine Learning Engineer
Designs custom neural architectures, loss functions, hyperparameter optimization, and statistical cross-validation loops.
Senior Data & Feature Engineer
Builds high-throughput feature extraction pipelines, data lakehouse integrations, and streaming transformation layers.
MLOps & Infrastructure Specialist
Manages Kubernetes cluster autoscaling, sub-20ms inference optimization, and 24/7 observability instrumentation.