Machine Learning
Machine Learning: End-to-End AI from Math to Metal
- PyTorch
- C++ (TensorRT/ONNX)
- Model Training
- Inference
“Merely integrating an AI API or running a pre-trained model is only half the job. I design custom architectures, process datasets, and train models from scratch in Python (PyTorch/TensorFlow). But when it comes to production, I leave Python's sluggishness and GIL limitations behind. I optimize (Quantization/Pruning) the trained models and run them on C++ inference engines with sub-millisecond latency, directly managing the GPU VRAM. True expertise means owning both the mathematics of the model and its physical footprint on the hardware.”
The Realities from Training to Inference
The Math & Data Abyss
Even the most advanced Transformer is garbage if trained on garbage data. Training isn't about calling libraries; it's about matching tensor shapes, preventing vanishing gradients, and commanding linear algebra.
The Python Bottleneck
Your model might work perfectly in the lab, but Python's Global Interpreter Lock (GIL) struggles under concurrent production requests. You must meet high traffic with multi-core C++ engines.
VRAM and Inference Cost
Spreading across massive GPU clusters during training is easy. The real challenge is optimizing that model to run in real-time on hardware-constrained environments without sacrificing quality.
End-to-End Operations
- 01
Custom Model Training
Designing and training deep learning architectures and neural networks from scratch on purpose-built datasets.
- 02
C++ Inference Engines
Converting trained models into ONNX or TensorRT formats to port them into low-latency environments capable of handling thousands of requests per second.
- 03
Local AI & Edge Computing
Machine learning systems running directly on the user's hardware (on-premise) to guarantee absolute data privacy and avoid cloud latency.
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