CANN for Edge AI Deployment Training Course

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

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

• Grasp the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimised for low-latency applications within 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimise 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 Greece (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 for superior inference and training performance.

This instructor-led training, available both online and on-site, is tailored for intermediate AI engineers and data scientists aiming to create and optimize neural network models leveraging Huawei's Ascend platform alongside the CANN toolkit.

Upon completing this training, participants will be equipped to:

• Establish and configure the CANN development environment.
• Create AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models within edge or cloud infrastructures.

Course Format

• Engaging lectures coupled with interactive discussions.
• Practical application of Huawei Ascend and the CANN toolkit in sample projects.
• Supervised exercises concentrating on model construction, training, and deployment.

Customization Opportunities

• For bespoke training aligned with your specific infrastructure or datasets, please get in touch with us to arrange.

CANN (Compute Architecture for Neural Networks) serves as 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 seeking to deploy trained AI models effectively on Huawei Ascend hardware. Participants will learn to utilize the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be equipped to:

• Grasp the CANN architecture and its critical role within the AI deployment pipeline.
• Convert and adapt models from widely used frameworks into Ascend-compatible formats.
• Utilize tools like ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
• Troubleshoot deployment issues and optimize performance on Ascend hardware.

Course Format

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

Course Customization Options

• For those interested in a customized training program, please get in touch with us to arrange it.

This instructor-led, live training in Greece (online or on-site) is designed for intermediate-level developers, data scientists, and tech enthusiasts seeking to gain practical skills in deploying AI models on edge devices for various applications.

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

• Comprehend the principles of Edge AI and its advantages.
• Establish and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Assess and enhance the performance of models deployed on the edge.
• Address ethical and security considerations within Edge AI applications.

This instructor-led, live training in Greece (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 processors designed to optimize both inference and training tasks for edge computing and datacenter environments.

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

Upon completing this course, participants will be able to:

• Configure and set up the development environments for BANGPy and Neuware.
• Develop and optimize models written in Python and C++ specifically for Cambricon MLUs.
• Deploy models to edge devices and datacenters operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Practical application of BANGPy and Neuware for development and deployment.
• Guided exercises concentrating on optimization, integration, and testing.

Customization Options

• To arrange a customized training session tailored to your specific Cambricon device model or use case, please get in touch with us.

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

This instructor-led live training, available either online or onsite, is tailored for beginner-level AI developers. It aims to elucidate how CANN integrates into the model lifecycle, spanning from training through to deployment, and illustrates its interaction with prominent frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehend the purpose and architectural structure of the CANN toolkit.
• Configure a development environment incorporating both CANN and MindSpore.
• Convert and deploy a fundamental AI model onto Ascend hardware.
• Establish a foundational knowledge base for future CANN optimisation or integration initiatives.

Course Format

• Engaging lectures coupled with interactive discussions.
• Practical hands-on labs focused on simple model deployment.
• Guided, step-by-step exploration of the CANN toolchain and integration points.

Course Customization Options

• For those seeking a tailored training experience, please reach out to us to arrange a bespoke session.

Huawei’s AI stack — spanning 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 online or onsite) is designed for beginner to intermediate-level technical professionals seeking to understand 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:

• Gain an understanding of the layered architecture of Huawei’s AI compute stack.
• Identify how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
• Position 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 exploring the model flow from MindSpore to CANN.

Customization Options for the Course

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

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

This instructor-led live training, available both online and onsite, is designed for advanced AI developers and system engineers aiming to boost inference performance by leveraging CANN's sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be able to:

• Comprehend CANN's runtime architecture and performance lifecycle.
• Utilise profiling tools and the Graph Engine for performance analysis and optimisation.
• Develop and optimise custom operators using TIK and TVM.
• Resolve memory bottlenecks and enhance model throughput.

Course Format

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

Customisation Options

• To arrange bespoke training for this course, please contact us directly.

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

This instructor-led live training, available online or onsite, is designed for intermediate-level AI professionals looking to develop, 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 utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and seamlessly integrate them into active pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored 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 utilizing real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please get in touch with us to arrange details.

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 (available online or onsite) is designed for advanced-level system developers who aim to build, deploy, and tune custom operators for AI models leveraging CANN’s TIK programming model and TVM compiler integration.

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

• Write and test custom AI operators utilizing the TIK DSL for Ascend processors.
• Integrate custom operations into the CANN runtime and execution graph.
• Employ TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and enhance instruction-level performance for custom computation 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.

Course Customization Options

• For customized training requests, please contact us to make arrangements.

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.

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

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

• Grasp the role of Edge AI in precision agriculture.
• Deploy AI-driven monitoring systems for crops and livestock.
• Create automated irrigation and environmental sensing solutions.
• Enhance agricultural efficiency through real-time Edge AI analytics.