LLMOps: Comparación de Openvino, ONNX, TensorRT y Pytorch para Inferencia
Compara el rendimiento de inferencia de modelos convertidos a ONNX, OpenVino y TensorRT frente a PyTorch nativo en GPU y CPU.
Applied Custom Vision for AI-Powered Camera Feed Analysis
Explore practical applications of AI-powered computer vision for workplace monitoring, from parking lot analytics to safety alerts, featuring Custom Vision edge computing and OpenVino demonstrations.
tinyML Vision Challenge - Intel-Luxonis DepthAI Platform Overview
Explore Intel and Luxonis' DepthAI platform for advanced vision solutions. Learn about AI inference, OpenVINO toolkit, and developing spatial edge applications using the LUX-ESP32 device.
Machine Learning in Fastly's Compute@Edge
Explore WebAssembly modules for efficient ML model execution in Fastly's Compute@Edge, covering wasi-nn extensions, host API revisions, and KServe protocol integration for enhanced performance.
End-to-End AI Developer Journey with Containerized Assets Using Intel DevCatalog and DevCloud
Explore containerized AI development using Intel DevCatalog and DevCloud. Learn to access, customize, and deploy AI assets on various Intel hardware, leveraging OpenVINO for training and inference.
LLMOps: Comparison of OpenVino, ONNX, TensorRT, and PyTorch Inference
Explore model conversion to ONNX, OpenVino, and Tensor-RT formats for optimized CPU and GPU inference, comparing performance against native PyTorch.
MLOps: OpenVino Quantized Pipeline for Grammatical Error Correction
Build a grammar error correction model using OpenVino, combining Roberta-based error detection and Flan-T5 correction, with quantization for efficient CPU inference.
Accelerate Your Deep Learning Inferencing with the Intel DL Boost Technology
Explore Intel® DL Boost technology for efficient deep learning inference acceleration using low-precision INT8 and VNNI instructions on 2nd gen Intel Xeon Scalable processors.
Deploying AI Models Using Intel AMX CPUs on VMware vSphere with Tanzu Kubernetes
Discover how to deploy AI models using Intel AMX CPUs on VMware vSphere, leveraging built-in accelerators and Tanzu Kubernetes for efficient, GPU-free machine learning workloads and real-time processing.
C++ Generative AI Inference - Production Ready Speed and Control
Optimize C++ for generative AI inference, enhancing speed and control. Explore techniques to boost performance and gain precise command over AI model execution in production environments.
Converting Alibaba Cloud Qwen2-VL Model to OpenVino IR Format - Spanish Tutorial
Aprende a convertir e implementar el modelo Qwen2-VL de Alibaba Cloud a formato OpenVino IR para optimizar inferencias en CPU, con ejemplos prácticos y código detallado.
Converting Qwen2-VL 2B Model to OpenVino IR Format for CPU Inference
Learn how to convert and optimize Qwen2-VL 2B model for CPU inference using OpenVino IR format, with practical implementation steps and performance insights.