PyTorch, ML.NET & Local LoRA/QLoRA Engineering
Generic LLM prompts cannot replace high-precision predictive models, domain-adapted weights, or real-time telemetry inference. Led by Principal Architect Zeeshan Dad Khan, we engineer end-to-end machine learning pipelines—from Exploratory Data Analysis (EDA) and LoRA/QLoRA fine-tuning on Apple Silicon M4 Max & Cloud GPUs to sub-10ms ML.NET and ONNX Runtime deployment inside enterprise .NET 10 ecosystems.
Full-Lifecycle ML Engineering
We take machine learning out of isolated Jupyter notebooks and engineer reproducible, type-safe training and inference systems integrated directly into your production software.
High-accuracy models start with uncompromised data hygiene. We audit raw SQL/NoSQL data lakes, build automated ETL cleansing pipelines, engineer high-signal features, and execute hybrid labeling playbooks that pair programmatic weak supervision with domain-expert verification.
Measurable Business Impact: Cuts dataset preparation time by up to 65% while eliminating silent training data leakage.
We train bespoke neural networks and gradient-boosted decision trees in PyTorch, TensorFlow, and Microsoft ML.NET, and perform parameter-efficient LoRA/QLoRA fine-tuning of Llama, Qwen, and Phi-4 on Apple Silicon M4 Max (MPS) workstations and cloud CUDA clusters.
Measurable Business Impact: Outperforms generic frontier LLMs on domain-specific tasks at 1/10th the compute footprint.
Turn historical transaction logs into forward-looking operational intelligence. We engineer customer churn prediction, multi-horizon inventory demand forecasting, dynamic pricing engines, and real-time financial fraud/anomaly detection models embedded directly in your ERP or SaaS backend.
Measurable Business Impact: 15–30% improvement in forecast precision and proactive churn intervention before revenue loss.
Process unstructured voice and text at enterprise scale without leaking sensitive audio to third parties. We build offline Whisper speech-to-text (STT) pipelines, multilingual named-entity recognition (NER), sentiment analysis, and legal/clinical summarization engines.
Measurable Business Impact: Sub-second offline voice transcription and automated document triage with zero cloud audio exposure.
Ingest and score high-frequency industrial sensor streams, medical device telemetry, and logistics fleet diagnostics in real time. We pair binary gRPC ingestion and ASP.NET Core 10 background channels with sliding-window ML.NET/ONNX anomaly detectors and live SignalR dashboards.
Measurable Business Impact: Predictive maintenance alerts seconds after sensor deviation, preventing unplanned equipment downtime.
Ensure your models remain fair, explainable, and accurate long after launch. We implement SHAP/LIME feature attribution, demographic parity audits, Population Stability Index (PSI) drift monitors, and automated shadow-retraining triggers.
Measurable Business Impact: Sustained production F1/AUC accuracy and audit-ready algorithmic fairness for regulated industries.
Battle-Tested ML Architecture
See how our production architectures combine Python PyTorch training rigor with compiled C# 13 / .NET 10 execution speed across real shipped products.
Deep convolutional and diffusion-based image restoration models require heavy GPU/MPS tensor math that stalls standard web servers and leaves users staring at frozen spinners.
In LuminaEdit, we engineered a hybrid pipeline: ASP.NET Core 10 Minimal APIs validate and queue incoming jobs to dedicated PyTorch MPS (Apple Silicon M4 Max) & CUDA worker processes, streaming step-by-step tensor inference progress back to the UI over SignalR WebSockets.
Zero thread-pool starvation, real-time sub-second progress telemetry, and hardware-accelerated neural restoration at scale.
Iterative multimodal transformations rapidly corrupt facial landmarks and biometric geometry when prior generated outputs are re-encoded in successive inference passes.
Powering VisionFlow AI across iOS, Android, and Web, our Anchor & Delta Context Engine pairs on-device biometric landmark extraction with an immutable Anchor tensor reference and a zero-key ASP.NET Core 10 BFF using Google Vertex AI Application Default Credentials (ADC).
10,000+ users, 4.8★ rating, and deterministic biometric identity preservation across multi-turn 2K studio generations.
Enterprise field apps and privacy-critical workflows cannot rely on cloud connectivity or pay per-minute API charges for speech recognition, OCR, and document classification.
We quantize Whisper Tiny, Phi-4, Qwen, and custom vision graphs into ONNX Runtime and Apple CoreML formats embedded inside .NET MAUI 10—complemented by our zero-upload client-side tooling proven on visionflowai.app/tools.
100% offline speech and vision execution on NPU hardware with zero cloud inference cost and complete GDPR/HIPAA compliance.
Data science teams often hand over monolithic Python scripts with hardcoded file paths, unvalidated inputs, and zero concurrency controls that crash under production traffic.
Through our 2-Week Onion Architecture sprint, we wrap custom ML models behind clean C# Domain Interfaces, strongly typed feature contracts, circuit breakers, and automated integration tests inside a containerized ASP.NET Core 10 solution.
Transforms brittle Jupyter notebooks into SLA-backed, horizontally scalable enterprise microservices in 10 business days.
ML Paradigm Comparison & Delivery Blueprint
How our hybrid PyTorch + ML.NET + ONNX architecture compares against naive LLM prompting and unhardened data-science scripts.
| Engineering Criterion | Generic Prompt-Only LLM API | Isolated Python Notebook Prototype | KCC Custom ML & ONNX/.NET 10 Pipeline |
|---|---|---|---|
| 1. Inference Latency & Throughput | 500ms–3,500ms per call; throttled by external token rate limits. | Single-threaded Flask/FastAPI bottlenecked by Python GIL under load. | Sub-10ms In-Process C# via ONNX Runtime / ML.NET + async PyTorch GPU workers. |
| 2. Tabular & Sensor Precision | Poor numerical calibration; prone to hallucinating forecasts. | High experimental accuracy, but fragile feature transformations in production. | Calibrated & Deterministic — Feature-parity pipelines with SHAP explainability. |
| 3. Domain Adaptation & IP Ownership | Zero weight ownership; competitors can copy your prompts overnight. | Weights trapped on local machines without reproducible CI/CD/CT. | 100% Proprietary IP — Custom LoRA/QLoRA adapters and versioned model registries. |
| 4. Data Privacy & Offline Edge | Requires transmitting raw payloads to external cloud vendors. | Rarely optimized or quantized for mobile/desktop NPU execution. | INT8/INT4 Quantized Edge — Runs locally on CoreML, DirectML & NNAPI. |
| 5. Drift Detection & Governance | Silent behavior shifts whenever cloud vendor updates base model. | No live telemetry; accuracy decays silently as market data shifts. | Automated PSI/KS Drift Alerts via OpenTelemetry + shadow retraining gates. |
We profile your historical datasets, quantify signal-to-noise ratios, establish a mathematical baseline metric (F1, RMSE, ROC-AUC), and implement automated ETL and hybrid labeling workflows.
We train candidate architectures across ML.NET, XGBoost, and PyTorch (leveraging Apple Silicon M4 Max MPS and cloud GPUs), performing hyperparameter sweeps and bias/fairness audits.
We compile winning models into quantized ONNX Runtime or CoreML artifacts—or wrap heavy PyTorch workers behind ASP.NET Core 10 Minimal APIs and SignalR streaming hubs using Onion Architecture.
We validate model predictions against live production traffic in shadow mode, wire up OpenTelemetry PSI/KS drift monitors, and deliver full source code, weights, and retraining runbooks.
Machine Learning Technology Stack
Purpose-built toolchains bridging Python deep learning research and mission-critical .NET 10 production systems.
End-to-end model development across PyTorch (CUDA & Apple Metal MPS), Microsoft ML.NET (AutoML & native C# estimators), TensorFlow/Keras, Scikit-learn, XGBoost, and LightGBM.
Parameter-efficient fine-tuning (PEFT) and INT8/INT4 quantization of Qwen, Microsoft Phi-4, Llama, and OpenAI Whisper speech models for ultra-fast local and edge execution.
High-speed dataframe transformations, automated schema validation, PostgreSQL pgvector, SQL Server, and Parquet data lakes.
Real-time telemetry ingestion and bidirectional inference streaming via ASP.NET Core 10 SignalR, gRPC, and System.Threading.Channels.
Feature attribution, demographic fairness testing, Kolmogorov-Smirnov drift alerts, and OpenTelemetry metrics.
Managed training and endpoints on Google Cloud Vertex AI, Azure Machine Learning, and reproducible GPU Docker containers.
Frequently Asked Questions
Answers on custom training vs. LLM APIs, Python-to-.NET integration, messy datasets, and edge deployment.
General-purpose LLM APIs excel at broad language reasoning, but they are too slow, expensive, and non-deterministic for high-volume tabular forecasting, real-time anomaly detection, or strict domain-specific classification. We train custom ML.NET, XGBoost, and PyTorch models—or fine-tune compact LoRA/QLoRA adapters on Qwen, Llama, and Phi-4—when you need sub-10ms latency, sovereign data privacy, and zero per-token cloud inference fees.
We eliminate the gap between Python data science notebooks and enterprise .NET software using two proven architectures: exporting trained models to quantized ONNX Runtime / Apple CoreML graphs for direct in-process C# execution, or running dedicated PyTorch MPS/CUDA background workers orchestrated by ASP.NET Core 10 Minimal APIs with live SignalR progress telemetry—the exact architecture powering our LuminaEdit platform.
Most enterprise ML failures stem from poor data hygiene rather than model selection. Every engagement starts with our Exploratory Data Analysis (EDA) and ETL engineering phase, where we deduplicate records, handle missing-value bias, and deploy hybrid labeling playbooks that combine programmatic weak supervision with human-in-the-loop SME verification.
Yes. Using INT8/INT4 quantization, ONNX Runtime, and Apple CoreML inside .NET MAUI 10, we deploy Whisper Tiny speech-to-text, biometric computer vision, and fine-tuned Small Language Models directly onto iOS, Android, macOS (Apple Silicon M-series), and Windows hardware with zero internet dependency.
We instrument every production model with OpenTelemetry feature-distribution logging, Kolmogorov-Smirnov (KS) and Population Stability Index (PSI) drift detectors, and automated shadow-evaluation pipelines (detailed further in our MLOps Consulting Services) that trigger retraining before accuracy drops impact your business KPIs.
Direct Principal Consultation
Tell us about your dataset, latency targets, and predictive analytics goals. Principal Architect Zeeshan Dad Khan personally reviews every inquiry within 24 hours on business days.
Zeeshan Dad Khan
