Curso de Adobe LiveCycle Designer

Huawei Ascend es una familia de procesadores de IA diseñados para inferencia y entrenamiento de alto rendimiento.

Este curso dirigido por un instructor (en línea o presencial) está destinado a ingenieros de AI intermedios y científicos de datos que desean desarrollar y optimizar modelos de redes neuronales utilizando la plataforma Ascend de Huawei y el kit de herramientas CANN.

Al final de este entrenamiento, los participantes podrán:

• Configurar y configurar el entorno de desarrollo CANN.
• Desarrollar aplicaciones AI usando MindSpore y flujos de trabajo CloudMatrix.
• Optimizar el rendimiento en Ascend NPUs utilizando operadores personalizados y tiling.
• Implementar modelos en entornos edge o cloud.

Formato del Curso

• Conferencia interactiva y discusión.
• Uso práctico de Huawei Ascend y el kit de herramientas CANN en aplicaciones de muestra.
• Ejercicios guiados enfocados en la construcción, entrenamiento e implementación del modelo.

Opciones de Personalización del Curso

• Para solicitar un entrenamiento personalizado para este curso basado en su infraestructura o conjuntos de datos, contáctenos para organizarlo.

CloudMatrix is Huawei’s unified AI development and deployment platform designed to support scalable, production-grade inference pipelines.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level AI professionals who wish to deploy and monitor AI models using the CloudMatrix platform with CANN and MindSpore integration.

By the end of this training, participants will be able to:

• Use CloudMatrix for model packaging, deployment, and serving.
• Convert and optimize models for Ascend chipsets.
• Set up pipelines for real-time and batch inference tasks.
• Monitor deployments and tune performance in production settings.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of CloudMatrix with real deployment scenarios.
• Guided exercises focused on conversion, optimization, and scaling.

Course Customization Options

• To request a customized training for this course based on your AI infrastructure or cloud environment, please contact us to arrange.

Biren AI Accelerators are high-performance GPUs designed for AI and HPC workloads with support for large-scale training and inference.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers who wish to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.

By the end of this training, participants will be able to:

• Understand Biren GPU architecture and memory hierarchy.
• Set up the development environment and use Biren’s programming model.
• Translate and optimize CUDA-style code for Biren platforms.
• Apply performance tuning and debugging techniques.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Biren SDK in sample GPU workloads.
• Guided exercises focused on porting and performance tuning.

Course Customization Options

• To request a customized training for this course based on your application stack or integration needs, please contact us to arrange.

Cambricon MLUs (Machine Learning Units) are specialized AI chips optimized for inference and training in edge and datacenter scenarios.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

By the end of this training, participants will be able to:

• Set up and configure the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge and data center devices running Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimization, integration, and testing.

Course Customization Options

• To request a customized training for this course based on your Cambricon device model or use case, please contact us to arrange.

Esta capacitación en vivo dirigida por un instructor en Guatemala (en línea o en el sitio) está dirigida a administradores de sistemas de nivel principiante y profesionales de TI que deseen instalar, configurar, administrar y solucionar problemas de entornos CUDA.

Al final de esta capacitación, los participantes serán capaces de:

• Comprender la arquitectura, los componentes y las capacidades de CUDA.
• Instalar y configurar entornos CUDA.
• Gestione y optimice los recursos de CUDA.
• Depurar y solucionar problemas comunes de CUDA.

Esta formación impartida por un instructor en Guatemala (en línea o presencial) está dirigida a desarrolladores de nivel intermedio que deseen utilizar CUDA para construir aplicaciones Python que se ejecuten en paralelo en tarjetas NVIDIA GPU.

Al final de esta capacitación, los participantes serán capaces de:

• Utilizar el compilador Numba para acelerar aplicaciones Python que se ejecutan en GPUs de NVIDIA.
• Crear, compilar y lanzar kernels CUDA personalizados.
• Gestionar la memoria de GPU.
• Convertir una aplicación basada en CPU en una aplicación acelerada por GPU.

Chinese GPU architectures such as Huawei Ascend, Biren, and Cambricon MLUs offer CUDA alternatives tailored for local AI and HPC markets.

This instructor-led, live training (online or onsite) is aimed at advanced-level GPU programmers and infrastructure specialists who wish to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

By the end of this training, participants will be able to:

• Evaluate compatibility of existing CUDA workloads with Chinese chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Compare performance and identify optimization points across platforms.
• Address practical challenges in cross-architecture support and deployment.

Format of the Course

• Interactive lecture and discussion.
• Hands-on code translation and performance comparison labs.
• Guided exercises focused on multi-GPU adaptation strategies.

Course Customization Options

• To request a customized training for this course based on your platform or CUDA project, please contact us to arrange.

Esta capacitación en vivo dirigida por un instructor en Guatemala (en línea o en el sitio) está dirigida a diseñadores web que desean usar Maya para crear animaciones 3D.

Al final de esta formación, los participantes serán capaces de:

• Crea modelos y texturas realistas en Maya.
• Anima y renderiza proyectos para una reproducción de alta calidad.
• Simula efectos naturales como el agua y el humo.

WebGL (Web Graphics Library) es una API de JavaScript para representar gráficos en 3D dentro de un navegador web sin el uso de complementos.

En esta capacitación en vivo dirigida por un instructor, los participantes aprenderán cómo generar imágenes de computadora realistas usando gráficos 3D a medida que avanzan en la creación de una aplicación animada en 3D que se ejecuta en un navegador.

Al final de esta capacitación, los participantes podrán:

• Comprender y utilizar las distintas funciones de WebGL, incluidas mallas, transformaciones, cámaras, materiales, iluminación y animación.
• Animar objetos con WebGL
• Crea objetos 3D usando WebGL

Audiencia

• Desarrolladores

Formato del curso.

• Lectura parcial, discusión parcial, ejercicios y práctica práctica intensiva.

Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each offering unique acceleration and profiling tools for production-scale AI workloads.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI infrastructure and performance engineers who wish to optimize model inference and training workflows across multiple Chinese AI chip platforms.

By the end of this training, participants will be able to:

• Benchmark models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory/compute inefficiencies.
• Apply graph-level, kernel-level, and operator-level optimizations.
• Tune deployment pipelines to improve throughput and latency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of profiling and optimization tools on each platform.
• Guided exercises focused on practical tuning scenarios.

Course Customization Options

• To request a customized training for this course based on your performance environment or model type, please contact us to arrange.