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A practical, 1,500+ word engineering guide to accelerating AI model inference speed using ONNX Runtime and NVIDIA TensorRT in Python web services — covering model export, graph optimization, FP16/INT8 precision, and C++ engine execution.
A practical, 1,500+ word engineering guide to computer vision pipelines in Python — combining OpenCV image processing, torchvision neural networks, object detection (YOLO), and web API integration.
A practical, 1,500+ word engineering guide to serving PyTorch and Hugging Face models inside FastAPI microservices — async request handling, lifespan context startup, batch processing, GPU concurrency, and Docker packaging.
A practical, 1,500+ word engineering guide to fine-tuning open-source LLMs using PyTorch, Hugging Face PEFT, and LoRA (Low-Rank Adaptation) — parameter-efficient training, dataset formatting, QLoRA 4-bit setup, and model merging.
A practical, 1,500+ word engineering tutorial for Python developers using Hugging Face Transformers — loading open-source LLMs, computing text embeddings, quantization with bitsandbytes, and pipeline optimization.
A practical, 1,500+ word engineering guide for web developers learning PyTorch — covering model loading, GPU memory management, dataset loaders, fine-tuning loops, and serving via web APIs.
A comprehensive, 1,600+ word technical comparison of PyTorch and TensorFlow for backend engineers and software architects — covering graph execution paradigms, debugging experience, production serving, Hugging Face ecosystem, and career positioning in 2026.
A practical, 1,500+ word engineering guide to Scikit-Learn for web product developers — building classification models, regression predictors, dataset preprocessing pipelines, cross-validation, and joblib serialization.
A practical, 1,500+ word engineering evaluation comparing TensorFlow Serving and TorchServe for enterprise model deployment — dynamic batching, multi-model hosting, gRPC vs REST endpoints, and Docker container orchestration.
A practical, 1,500+ word engineering guide to generating text vector embeddings using PyTorch and Sentence-Transformers — sentence pooling layers, cosine similarity math, batch vectorization, and pgvector storage.