CANN for Edge AI Deployment Training Course

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.

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

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

The course "6G and the Intelligent Edge" is a forward-thinking program that delves into the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing. This integration aims to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available online or on-site) is designed for intermediate-level IT architects who wish to understand and design next-generation distributed architectures by leveraging the synergy of 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

• Grasp how 6G will revolutionize edge computing and IoT architectures.
• Design distributed systems that ensure ultra-low latency, high bandwidth, and autonomous operations.
• Incorporate AI and data analytics at the edge to facilitate intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Assess business and operational models enabled by the convergence of 6G and edge technologies.

Format of the Course

• Interactive lectures and discussions.
• Case studies and practical architecture design exercises.
• Hands-on simulations with optional edge or container tools.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.

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

• Explore advanced techniques in Edge AI model development and optimization.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Optimize performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments.

The Huawei Ascend series is a collection of AI processors tailored for efficient inference and training tasks.

This instructor-led live training (delivered online or at your location) targets intermediate-level AI engineers and data scientists aiming to create and refine neural network models with the Huawei Ascend platform and CANN toolkit.

Upon completion, participants will be able to:

• Establish and configure the CANN development environment.
• Create AI applications using MindSpore and CloudMatrix workflows.
• Tune performance on Ascend NPUs with custom operators and tiling techniques.
• Deploy models in both edge and cloud settings.

Course Format

• Interactive lectures and discussions.
• Practical use of Huawei Ascend and the CANN toolkit through sample applications.
• Guided exercises centered on model creation, training, and deployment.

Customization Options for the Course

• If you wish to tailor this course based on your specific infrastructure or datasets, please contact us to arrange a customized session.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI compute stack designed for deploying and optimizing AI models on Ascend AI processors.

This instructor-led, live training (available online or onsite) is targeted at intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models to Huawei Ascend hardware using the CANN toolkit, along with tools such as MindSpore, TensorFlow, or PyTorch.

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

• Gain a comprehensive understanding of the CANN architecture and its significance in the AI deployment pipeline.
• Convert and adapt models from popular frameworks into formats compatible with Ascend processors.
• Utilize tools like ATC, OM model conversion, and MindSpore for inference at the edge and in the cloud.
• Identify and resolve deployment issues while optimizing performance on Ascend hardware.

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on lab sessions using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led, live training in the UAE (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to enhance performance in both inference and training tasks within edge computing and data center environments.

This instructor-led live training session (conducted either online or at your location) is tailored for intermediate developers looking to create and deploy AI models using the BANGPy framework alongside Neuware SDK on Cambricon MLU hardware.

Upon completion of this course, participants will be able to:

• Establish and configure development environments for BANGPy and Neuware.
• Create and refine Python- and C++-based models specifically for Cambricon MLUs.
• Deploy these models onto edge devices and data centers that utilize Neuware runtime.
• Incorporate ML workflows with features optimized for MLU acceleration.

Course Format

• Engaging lectures combined with interactive discussions.
• Practical hands-on experience using BANGPy and Neuware for both development and deployment tasks.
• Guided exercises centered on optimization, integration, and testing processes.

Customization Options

• If you require a customized training session based on your specific Cambricon device model or use case, please reach out to us for further arrangements.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — offers a tightly integrated AI development and deployment environment optimized for Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to understand how the CANN and MindSpore components work together to support AI lifecycle management and infrastructure decisions.

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

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN supports model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and toolchain in relation to industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-prem environments.

Format of the Course

• Interactive lecture and discussion.
• Live system demos and case-based walkthroughs.
• Optional guided labs on model flow from MindSpore to CANN.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI compute foundation that allows developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers and system engineers who wish to optimize inference performance using CANN’s advanced toolset, including the Graph Engine, TIK, and custom operator development.

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

• Understand CANN's runtime architecture and performance lifecycle.
• Use profiling tools and Graph Engine for performance analysis and optimization.
• Create and optimize custom operators using TIK and TVM.
• Resolve memory bottlenecks and improve model throughput.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with real-time profiling and operator tuning.
• Optimization exercises using edge-case deployment examples.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) provides powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, especially on Huawei Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI practitioners who wish to build, deploy, and optimize vision and language models using the CANN SDK for production use cases.

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

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Use CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks like detection, classification, and sentiment analysis.
• Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab with model deployment and performance profiling.
• Live pipeline design using real CV and NLP use cases.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

CANN TIK (Tensor Instruction Kernel) and Apache TVM enable advanced optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at advanced-level system developers who wish to build, deploy, and tune custom operators for AI models using CANN’s TIK programming model and TVM compiler integration.

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

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom ops into the CANN runtime and execution graph.
• Use TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming.

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

• Understand the role of Edge AI in precision agriculture.
• Implement AI-driven crop and livestock monitoring systems.
• Develop automated irrigation and environmental sensing solutions.
• Optimize agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.

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

• Understand the role and benefits of Edge AI in autonomous systems.
• Develop and deploy AI models for real-time processing on edge devices.
• Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
• Design and optimize control systems using Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.