CANN for Edge AI Deployment Training Course

This instructor-led, live training in Sweden (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks enhance Edge AI applications.

By the end 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 for efficient performance on edge devices.

This forward-thinking course delves into how 6G wireless technology converges with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, low-latency, and adaptive infrastructures.

Delivered via live, instructor-led sessions (available online or onsite), this program is designed for intermediate-level IT architects seeking to grasp and architect next-generation distributed systems by harnessing the combined power of 6G connectivity and intelligent edge platforms.

After completing this course, participants will be equipped to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Architect distributed systems capable of ultra-low latency, high bandwidth, and autonomous functions.
• Implement AI and data analytics at the edge to enable smart decision-making.
• Develop scalable, secure, and resilient 6G-ready edge infrastructures.
• Assess business and operational models that arise from the convergence of 6G and edge networks.

Course Format

• Interactive lectures and group discussions.
• Case studies and hands-on architecture design exercises.
• Practical simulations using optional edge or container-based tools.

Customization Options

• For tailored training arrangements for this course, please reach out to us.

This instructor-led, live training in Sweden (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 range of AI processors engineered for high-performance inference and training.

This instructor-led training (available online or onsite) targets intermediate-level AI engineers and data scientists looking to develop and optimize neural network models using Huawei’s Ascend platform alongside the CANN toolkit.

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

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

Course Format

• Interactive lectures and discussions.
• Practical application of Huawei Ascend and the CANN toolkit in sample scenarios.
• Guided exercises centered on model building, training, and deployment.

Customization Options

• For customized training tailored to your specific infrastructure or datasets, please contact us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as 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) targets intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models to Huawei Ascend hardware. The course utilizes the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

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

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

Course Format

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

Customization Options

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

This instructor-led, live training in Sweden (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 Sweden (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 optimized for both inference and training tasks in edge computing and data center environments.

This instructor-led live training, available online or onsite, is designed for intermediate-level developers who want to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completion of 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 and data center devices 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 focusing on optimization, integration, and testing.

Customization Options

• For customized training 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 specialized toolkit for AI computing, enabling the compilation, optimization, and deployment of AI models on Ascend AI processors.

This instructor-led live training session, available in both online and onsite formats, is designed for beginner-level AI developers. The course helps participants grasp how CANN integrates into the model lifecycle—from training through to deployment—and demonstrates its interoperability with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be able to:

• Comprehend the purpose and underlying architecture of the CANN toolkit.
• Configure a development environment utilizing CANN alongside MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration initiatives.

Course Format

• Engaging lectures combined with interactive discussions.
• Practical hands-on labs focused on simple model deployment.
• Step-by-step guidance through the CANN toolchain and its integration points.

Customization Options

• For inquiries regarding customized training for this course, please reach out to us to arrange details.

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

This instructor-led, live training (available online or on-site) is designed for beginner-to-intermediate level technical professionals seeking to understand how the CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.

Upon completing this training, participants will be able to:

• Comprehend the layered architecture of Huawei’s AI computing stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and 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 on the model flow from MindSpore to CANN.

Course Customization Options

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

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

This instructor-led live training session, available both online and onsite, is designed for advanced-level AI developers and system engineers eager to enhance inference performance utilizing CANN’s sophisticated toolset. Key areas include the Graph Engine, TIK, and custom operator development.

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

• Gaining a comprehensive understanding of CANN's runtime architecture and performance lifecycle.
• Employing profiling tools and the Graph Engine to conduct performance analysis and optimization.
• Developing and optimizing custom operators using TIK and TVM.
• Identifying memory bottlenecks and enhancing model throughput.

Course Format

• Interactive lectures accompanied by discussions.
• Practical labs featuring real-time profiling and operator tuning.
• Optimization exercises utilizing deployment examples for edge cases.

Course Customization Options

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

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

This instructor-led training, available online or onsite, is designed for 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.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for both edge and cloud deployment scenarios.

Course Format

• Interactive lectures combined with practical demonstrations.
• 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 to discuss your specific needs.

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate 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 system developers who aim to build, deploy, and tune custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.

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

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

Customization Options

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

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

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

• Grasping the significance of Edge AI in precision agriculture.
• Deploying AI-powered systems for monitoring crops and livestock.
• Creating automated irrigation and environmental sensing solutions.
• Enhancing agricultural efficiency through real-time Edge AI analytics.

This instructor-led, live training in Sweden (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.