The Machine Learning Engineer Courses

Inferencia en CPU con Microsoft Florence 2 ONNX en C# - LLMOPs

Aprende a realizar inferencia en CPU con el modelo Florence 2 Large en formato ONNX utilizando C#. Explora técnicas avanzadas de machine learning y ciencia de datos.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

CPU Inference with ViT ONNX Model in Azure ML Managed Endpoint - AKS

Deploy a Vision Transformer model for CPU inference on Azure Kubernetes Service using ONNX format and Azure ML SDK v2. Learn to create managed endpoints in Azure ML Studio.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

MLOPS: Inferencia de Modelo VIT ONNX en Azure ML Managed Endpoint - Español

Aprende a realizar inferencia en Azure Kubernetes Services utilizando un modelo Vision Transformer convertido a ONNX con Azure Machine Learning Studio y Python SDK v2.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

MLOps: Inference ONNX Model in Azure ML Managed Endpoint AKS

Deploy ONNX models to Azure ML Managed Endpoints (AKS) for efficient inference. Learn step-by-step process using Python SDK v2 to optimize machine learning operations.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

MLOPS: Inferencia de Modelo ONNX en Azure ML Managed Endpoint AKS

Aprende a realizar inferencia en Azure Kubernetes Services utilizando un modelo ONNX y onnxruntime, creando un Managed Endpoint con Python SDK v2.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

LLMOps: Intel OpenVino Toolkit Inference CPU Stable Diffusion Model

Optimize Stable Diffusion inference on CPU using Intel OpenVINO toolkit for efficient text-to-image and image-to-image generation with Transformers Optimum pipeline.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

LLMOps - Intel OpenVino Toolkit Inferencia CPU Stable Diffusion Model

Genera imágenes con Stable Diffusion usando OpenVino ToolKit en CPU. Aprende a combinar texto e imágenes para crear arte visual único.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

NLP Spacy: PII/GDPR Detection with Presidio - Part 2 - Code and REST API

Develop a PII/GDPR detector using Microsoft Presidio and Spacy, implementing a REST API with FastAPI and CI/CD pipelines with Azure DevOps and Docker Compose.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

NLP Spacy: PII/GDPR Detection with Presidio - Part 2 Code and REST API

Desarrolla una aplicación para detectar información PII/GDPR utilizando Spacy, Presidio, FastAPI y Azure DevOps. Aprende a implementar pipelines de integración y despliegue.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

PII/GDPR Detection with Presidio and Spacy - Part 1: Architecture and Design

Explore PII/GDPR detection using Microsoft Presidio and Spacy, focusing on architecture and design for effective data protection implementation.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

NLP Spacy: PII/GDPR Detection with Presidio - Part 1 Architecture APP

Aprende a desarrollar una aplicación para detectar información regulada por PII y GDPR utilizando NLP Spacy y Presidio. Explora la arquitectura y tecnologías implementadas.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Computer Vision: OCR Tesseract in C# - Data Science and Machine Learning

Implement OCR using Tesseract library in C# for extracting text from images, enhancing your computer vision skills.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Computer Vision: OCR Tesseract in C# - Español

Aprende a implementar OCR con Tesseract en C# para reconocimiento de texto en imágenes. Explora esta poderosa herramienta de visión por computadora.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Object Detection with Faster R-CNN in C# Using ONNX on CPU

Implement object detection using Faster R-CNN in C# with ONNX format on CPU, leveraging machine learning for efficient image analysis.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

MLOPS: Object Detection con Faster RCNN en C# Formato ONNX en CPU

Desarrolla una aplicación de detección de objetos en C# utilizando Fast RCNN, ONNX y CPU para inferencia eficiente.

• YouTube
• 24 minutes
• Self-Paced
• Free Video