CANN for Edge AI Deployment Training Course

Aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists, this instructor-led live training in Denmark (online or onsite) examines how 5G networks drive Edge AI applications.

By the end of the session, participants will be equipped to:

• Comprehend the basics of 5G technology and its role in Edge AI.
• Implement AI models optimized for low-latency tasks in 5G settings.
• Establish real-time decision-making frameworks utilizing Edge AI and 5G links.
• Enhance AI workload efficiency on edge devices.

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

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

• Investigate advanced techniques in Edge AI model development and optimization.
• Implement state-of-the-art strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Optimize 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.

This live, instructor-led training is available Denmark (online or on-site) and is tailored for intermediate-level 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 the program, participants will be able to:

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

This live, instructor-led training available in Denmark (online or onsite) targets senior cybersecurity professionals, AI engineers, and IoT developers aiming to implement effective security measures and resilience strategies for Edge AI systems.

By the end of the session, participants will be able to:

• Assess security risks and vulnerabilities in Edge AI deployments.
• Utilize encryption and authentication techniques for data security.
• Design resilient Edge AI architectures resistant to cyber threats.
• Apply secure deployment strategies for AI models in edge settings.

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, available online or onsite, is tailored for beginner-level AI developers seeking to understand where CANN fits within the model lifecycle—from training to deployment—and how it integrates with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Grasp the purpose and architectural design of the CANN toolkit.
• Configure a development environment using CANN and MindSpore.
• Convert and deploy a simple AI model onto Ascend hardware.
• Acquire the foundational knowledge necessary for future CANN optimization or integration projects.

Course Format

• Engaging lectures and discussions.
• Practical hands-on labs focused on basic model deployment.
• Detailed walkthroughs of the CANN toolchain and its integration points.

Customization Options

• Interested in a customized training experience? Please reach out to us to arrange specific requirements.

Huawei’s comprehensive AI ecosystem, spanning from the foundational CANN SDK to the advanced MindSpore framework, provides a seamlessly integrated environment for developing and deploying AI solutions, specifically tuned for Ascend hardware.

Designed for technical professionals at both entry and intermediate levels, this instructor-led training—available online or in-person—offers a deep dive into how CANN and MindSpore collaborate to manage the AI lifecycle and inform critical infrastructure strategies.

Upon completion, participants will be equipped to:

• Grasp the hierarchical structure of Huawei’s AI computing architecture.
• Recognize how CANN facilitates model optimization and hardware-level execution.
• Assess the MindSpore framework and its associated tools against competing industry solutions.
• Strategically position Huawei’s AI suite within enterprise or hybrid cloud/on-premise settings.

Delivery Method

• Engaging lectures facilitated by open discussion.
• Practical demonstrations and scenario-driven analysis.
• Optional guided workshops exploring the model pathway from MindSpore to CANN.

Tailored Training Options

• For bespoke training requirements, please reach out to us to coordinate a customized program.

CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI computing platform, enabling developers to refine and optimize the execution of deployed neural networks on Ascend AI processors.

Designed for advanced AI developers and system engineers, this instructor-led live training (available online or onsite) focuses on maximizing inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

Upon completion, participants will be equipped to:

• Grasp the runtime architecture and performance lifecycle of CANN.
• Leverage profiling tools and the Graph Engine to conduct detailed performance analysis and optimization.
• Develop and refine custom operators utilizing TIK and TVM.
• Address memory bottlenecks to enhance overall model throughput.

Course Delivery Format

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

Customization Opportunities

• To explore tailored training options for this course, please get in touch with our team to discuss your specific requirements.

\r\n The CANN SDK (Compute Architecture for Neural Networks) equips developers with robust deployment and optimization capabilities for real-time AI applications in computer vision and NLP, specifically targeting Huawei Ascend hardware.\r\n<\/p>\r\n

\r\n This live, instructor-led training (available online or onsite) is designed for intermediate-level AI professionals looking to build, deploy, and optimize vision and language models for production environments using the CANN SDK.\r\n<\/p>\r\n

\r\n Upon completion of this course, participants will have the capability to:\r\n<\/p>\r\n

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• \r\n Deploy and fine-tune CV and NLP models leveraging CANN and AscendCL.\r\n
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• \r\n Utilize CANN tools for model conversion and seamless integration into live pipelines.\r\n
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• \r\n Enhance inference performance for key tasks such as detection, classification, and sentiment analysis.\r\n
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• \r\n Construct real-time CV\/NLP pipelines suitable for edge or cloud-based deployment scenarios.\r\n
\r\n<\/ul>\r\n

\r\n Course Format\r\n<\/p>\r\n

\r\n
• \r\n Engaging lectures combined with practical demonstrations.\r\n
\r\n
• \r\n Hands-on laboratory sessions focused on model deployment and performance profiling.\r\n
\r\n
• \r\n Live pipeline design exercises using actual CV and NLP use cases.\r\n
\r\n<\/ul>\r\n

\r\n Course Customization\r\n<\/p>\r\n

\r\n
• \r\n For tailored training options, please reach out to us to discuss your specific requirements.\r\n
\r\n<\/ul>

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

Designed for advanced-level system developers, this live, instructor-led training (available online or onsite) focuses on building, deploying, and fine-tuning custom operators. It leverages CANN’s TIK programming model alongside TVM compiler integration to achieve high-performance results.

Upon completing this training, participants will have the capability to:

• Develop and verify custom AI operators using the TIK DSL tailored for Ascend processors.
• Seamlessly integrate custom operations into the CANN runtime and execution graphs.
• Leverage TVM for operator scheduling, automated tuning, and performance benchmarking.
• Diagnose and refine instruction-level performance for specific computation patterns.

Course Delivery Format

• Engaging interactive lectures accompanied by live demonstrations.
• Practical coding exercises involving TIK and TVM pipelines.
• Empirical testing and tuning conducted on Ascend hardware or simulators.

Tailored Course Options

• To explore customized training options for this course, please reach out to us for arrangements.

Harness the transformative potential of AI combined with the agility of edge computing in this comprehensive course. You will learn to deploy AI models directly on edge devices, gaining expertise from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training empowers you to optimize AI performance for real-time processing and decision-making at the edge.

Aimed at beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers seeking to develop and deploy Edge AI solutions for smart farming, this live, instructor-led training in Denmark can be delivered either online or onsite.

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

• Comprehend the role of Edge AI in precision agriculture.
• Establish AI-driven monitoring systems for crops and livestock.
• Engineer automated irrigation and environmental sensing solutions.
• Improve agricultural efficiency via real-time Edge AI analytics.