CANN for Edge AI Deployment Training Course

This instructor-led, live training in Vietnam (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who want to explore how 5G networks accelerate Edge AI applications.

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

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

"6G and the Intelligent Edge" is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

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

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

• Understand how 6G will transform edge computing and IoT architectures.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge for intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate business and operational models enabled by 6G-edge convergence.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation 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 Vietnam (online or onsite) targets 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:

• Investigate advanced techniques for developing and optimizing Edge AI models.
• Apply cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Discover innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments.

Huawei Ascend comprises a series of AI processors engineered for high-performance inference and model training.

This instructor-led, live training (available online or onsite) is designed for intermediate-level AI engineers and data scientists who aim to develop and optimize neural network models using Huawei’s Ascend platform alongside the CANN toolkit.

Upon completing this course, participants will be capable of:

• Configuring and setting up the CANN development environment.
• Creating AI applications via MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through custom operators and tiling techniques.
• Deploying models into edge or cloud environments.

Course Format

• Interactive lectures and discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Guided exercises targeting model construction, training, and deployment.

Customization Options

• For customized training tailored to your specific infrastructure or datasets, please contact us to arrange a session.

CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute stack, designed to deploy and optimize AI models on Ascend AI processors.

This instructor-led training session, available both online and onsite, targets intermediate-level AI developers and engineers looking to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit along with tools like MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be equipped to:

• Gain a solid understanding of the CANN architecture and its critical role in the AI deployment pipeline.
• Convert and adapt models from popular frameworks into formats compatible with Ascend processors.
• Utilize tools such as ATC, OM model conversion utilities, and MindSpore for inference on both edge devices and cloud environments.
• Diagnose deployment challenges and optimize performance specifically for Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical hands-on labs using CANN tools alongside Ascend simulators or physical devices.
• Real-world deployment scenarios based on actual AI models.

Customization Options for the Course

• For those interested in a customized training version of this course, please contact us to arrange details.

This live, instructor-led training in Vietnam (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.

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

• Understand the fundamental principles and benefits of Edge AI.
• 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 Vietnam (online or onsite) is designed for advanced-level cybersecurity experts, AI engineers, and IoT developers who aim to implement strong security measures and resilience strategies for Edge AI systems.

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

• Analyze security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques to ensure data protection.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure strategies for deploying AI models in edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for high-performance inference and training in both edge computing and data center environments.

This instructor-led, live training session (available online or onsite) is tailored for intermediate-level developers looking to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Set up and configure development environments for BANGPy and Neuware.
• Develop and optimize models written in Python and C++ for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate machine learning workflows with specific MLU acceleration features.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises using BANGPy and Neuware for development and deployment.
• Guided practice focusing on optimization, integration, and testing.

Customization Options

• To request a customized version of this course tailored to your specific Cambricon device model or use case, please contact us to arrange.

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

This instructor-led live training session is available either online or onsite. It targets beginner-level AI developers looking to grasp how CANN integrates into the model lifecycle—from training through to deployment—and how it collaborates with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be capable of:

• Comprehending the purpose and architecture of the CANN toolkit.
• Establishing a development environment utilizing CANN and MindSpore.
• Converting and deploying a simple AI model onto Ascend hardware.
• Acquiring foundational knowledge to support future CANN optimization or integration initiatives.

Format of the Course

• Interactive lectures and discussions.
• Hands-on labs focused on simple model deployment.
• Step-by-step guidance through the CANN toolchain and its integration points.

Course Customization Options

• For those interested in customized training for this course, please reach out to us to arrange it.

Huawei’s AI stack — spanning from the low-level CANN SDK to the high-level MindSpore framework — provides a tightly integrated environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.

This instructor-led, live training (available online or onsite) targets technical professionals at beginner to intermediate levels who want to comprehend how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decision-making.

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

• Comprehend the layered architecture of Huawei’s AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
• Contextualize Huawei's AI stack within enterprise or cloud/on-premises environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs covering the model flow from MindSpore to CANN.

Course Customization Options

• For requests regarding customized training for this course, please contact us to make arrangements.

CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, empowering developers to fine-tune and enhance the performance of deployed neural networks on Ascend AI processors.

This instructor-led live training session (available online or onsite) targets advanced-level AI developers and system engineers who aim to optimize inference performance by leveraging CANN’s sophisticated toolset, including the Graph Engine, TIK, and capabilities for custom operator development.

Upon completing this training, participants will be able to:

• Gain a comprehensive understanding of CANN's runtime architecture and performance lifecycle.
• Utilize profiling tools and the Graph Engine to conduct thorough performance analysis and optimization.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and significantly improve model throughput.

Course Format

• Interactive lectures and discussions.
• Hands-on labs featuring real-time profiling and operator tuning.
• Optimization exercises based on edge-case deployment scenarios.

Course Customization Options

• To request a tailored training session for this course, please contact us to arrange it.

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

This instructor-led live training, available online or onsite, is targeted at intermediate-level AI professionals looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completion of this training, participants will be capable of:

• Deploying and optimizing computer vision (CV) and NLP models using CANN and AscendCL.
• Utilizing CANN tools to convert models and seamlessly integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises using real-world CV and NLP use cases.

Course Customization Options

• For customized training arrangements, please contact us directly.

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

This instructor-led, live training session—available both online and on-site—is designed for experienced system developers aiming to construct, deploy, and fine-tune custom operators for AI models leveraging CANN’s TIK programming model and TVM compiler integration.

Upon completing this training, participants will achieve the following:

• Develop and test custom AI operators using the TIK DSL for Ascend processors.
• Embed custom operations into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, auto-tuning, and benchmarking tasks.
• Debug and optimize instruction-level performance tailored to specific computation patterns.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical coding exercises involving operators via TIK and TVM pipelines.
• Testing and tuning performed on Ascend hardware or within simulator environments.

Customization Options for the Course

• For those seeking a tailored training experience for this course, please reach out to us to arrange it.

This instructor-led, live training in Vietnam (online or onsite) is designed for beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers interested in developing and deploying Edge AI solutions for smart farming.

By the end of this course, participants will be capable of:

• Understanding the role of Edge AI in precision agriculture.
• Implementing AI-driven crop and livestock monitoring systems.
• Developing automated irrigation and environmental sensing solutions.
• Optimizing agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training Vietnam (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who want to harness Edge AI for cutting-edge autonomous system solutions.

By the end of this course, 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.