Production-Grade ML Model Engineering & MLOps
Bridge the gap between experimental Jupyter notebooks and mission-critical production systems. We optimize, quantize, compile, and serve models for high-throughput, sub-millisecond inference.
Engineered for Extreme Speed, Scale, and Cost Efficiency
An accurate model is useless if it is too slow or too expensive to serve in production. BigIntend applies deep hardware-level optimization — including weight pruning, INT8 quantization, and TensorRT engine compilation — to slash inference latencies by 85% and reduce cloud GPU bills by up to 70%.
85% Latency Reduction
Sub-millisecond inference speeds for high-frequency applications.
70% Cloud GPU Savings
Pack multiple compressed models onto smaller, cost-effective GPU instances.
Enterprise MLOps & Autoscaling
Zero-downtime rolling model updates and automated spot-instance scaling.
Our ML Model Engineering Capabilities
Hardware Compilation (TensorRT & OpenVINO)
Compile PyTorch and TensorFlow models into hardware-specific execution graphs optimized for NVIDIA and Intel chips.
- TensorRT Engine Generation & Layer Fusion
- Sub-10ms P99 Latency SLA
Post-Training Quantization & Pruning
Compress FP32 weights to INT8 and FP4 precision using calibration datasets with zero perceptible loss in accuracy.
- Post-Training Quantization (PTQ)
- Quantization-Aware Training (QAT) & Weight Pruning
ONNX Model Portability & Cross-Platform Serving
Convert proprietary model checkpoints into open standard ONNX format for seamless cross-cloud and edge deployment.
- Cross-Platform ONNX Runtime Optimization
- C++ Engine Integration for Extreme Speed
Triton Inference Server Architecture
Deploy NVIDIA Triton Inference Server supporting concurrent model execution, dynamic batching, and multi-GPU routing.
- Dynamic Request Batching & Queuing
- Multi-Model GPU Memory Sharing
Automated Drift Monitoring & Data Quality Alerts
Continuous telemetry tracking feature drift (Evidently AI), prediction latency spikes, and accuracy degradation in real-time.
- Automated Retraining Trigger Pipelines
- Real-Time CloudWatch & Prometheus Dashboards
A/B Testing & Shadow Deployment
Safely test new model versions in production using canary rollouts and shadow traffic mirroring before full release.
- Canary & Shadow Traffic Routing
- Automated Rollback Safeguards
ML Engineering, Inference & MLOps Tools
Our 5-Step Model Optimization Lifecycle
Profiling & Latency Bottleneck Analysis
We profile model execution graphs to identify memory bandwidth bottlenecks and compute stalls.
Model Pruning & Quantization Calibration
We apply INT8 post-training quantization and verify numerical accuracy against validation benchmarks.
Engine Compilation & Optimization
We compile models with TensorRT / ONNX Runtime to enable kernel fusion and memory pooling.
Triton Server & Dynamic Batching Setup
We configure concurrent GPU worker processes and dynamic batching for peak query throughput.
Production Rollout & Telemetry Dashboard
We deploy auto-scaling Kubernetes clusters with real-time latency and drift alerting.
Frequently Asked Questions
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