CANN for Edge AI Deployment Training Course

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

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

• Grasp the fundamentals of 5G technology and its influence 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 to ensure efficient performance on edge devices.

This forward-looking course examines how 6G wireless technologies converge with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

This instructor-led live training, available online or onsite, is designed for intermediate-level IT architects seeking to understand and design next-generation distributed architectures that leverage the combined power of 6G connectivity and intelligent edge systems.

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

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

Format of the Course also allows for the evaluation of participants.

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using optional edge or container tools.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

This instructor-led, live training in France (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.

Huawei Ascend comprises a series of AI processors engineered to deliver high-performance capabilities for both inference and training tasks.

This instructor-led live training, available online or on-site, is designed for intermediate-level AI engineers and data scientists aiming to develop and optimize neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.

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

• Configure and establish the CANN development environment.
• Create AI applications leveraging MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the use of tiling and custom operators.
• Deploy models across cloud or edge environments.

Course Format

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

Course Customization Options

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange.

CANN (Compute Architecture for Neural Networks) represents Huawei's AI compute stack designed for the efficient deployment and optimization of AI models on Ascend AI processors.

This instructor-led, live training, available both online and onsite, targets intermediate-level AI developers and engineers aiming to deploy trained AI models efficiently on Huawei Ascend hardware. The course leverages the CANN toolkit alongside established tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be equipped to:

• Grasp the CANN architecture and its critical function within the AI deployment pipeline.
• Convert and adapt models from popular frameworks into formats compatible with Ascend.
• Utilize tools such as ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
• Diagnose deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Hands-on lab exercises utilizing CANN tools and Ascend simulators or devices.
• Practical deployment scenarios grounded in real-world AI models.

Course Customization Options

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

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

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

• Grasping the principles of Edge AI and its associated advantages.
• Establishing and configuring the edge computing environment.
• Creating, training, and optimizing AI models for edge deployment.
• Implementing functional AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed at the edge.
• Tackling ethical and security issues inherent in Edge AI applications.

This instructor-led, live training in France (online or onsite) targets advanced cybersecurity professionals, AI engineers, and IoT developers seeking to implement robust security measures and resilience strategies for Edge AI systems.

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

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Deploy encryption and authentication methods to ensure data protection.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI processors designed to optimize both inference and training workloads for edge computing and data center environments.

This instructor-led live training (available online or onsite) targets intermediate developers looking to build and deploy AI models utilizing the BANGPy framework and the Neuware SDK on Cambricon MLU hardware.

Upon completion of this course, 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 and data center devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on experience with BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Customization Options

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

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

This instructor-led live training (available online or onsite) targets beginner-level AI developers who want to understand how CANN integrates into the model lifecycle, from training through to deployment, and how it interacts with frameworks such as 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 using 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 also allows for the evaluation of participants.

• 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, ranging from the low-level CANN SDK to the high-level MindSpore framework, provides a tightly integrated environment for AI development and deployment, optimized specifically for Ascend hardware.

This instructor-led live training, available either online or on-site, targets beginner to intermediate technical professionals seeking to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.

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

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

Format of the Course also allows for the evaluation of participants.

• 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

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

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

This instructor-led live training, available either online or onsite, is designed for advanced AI developers and system engineers seeking to optimize inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

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

• Grasp CANN's runtime architecture and its performance lifecycle.
• Utilize profiling tools and the Graph Engine to analyze and optimize performance.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Course Format

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

Customization Options

• For personalized training arrangements for this course, please get in touch with us.

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

This instructor-led training, available both online and onsite, targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into active pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Live pipeline design utilizing real-world CV and NLP use cases.

Customization Options

• For a customized version of this course, please reach out to us to arrange your training.

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

This instructor-led, live training session (available online or onsite) is designed for advanced system developers who aim to create, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.

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

• Writing and testing custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for specific computational patterns.

Course Format

• Interactive lectures and demonstrations.
• Practical coding exercises for operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Customization Options for the Course

• To request a customized training version of this course, please contact us to make arrangements.

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

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

• Grasp the role and advantages of Edge AI in autonomous systems.
• Create 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 utilizing Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.