CANN for Edge AI Deployment Training Course

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

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

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

The course '6G and the Intelligent Edge' offers a forward-looking perspective on integrating 6G wireless technologies 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 who aim to comprehend and design next-generation distributed architectures that leverage the synergy of 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

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

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 Serbia (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 is a series of AI processors engineered for high-performance inference and training tasks.

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

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

• Setting up and configuring the CANN development environment.
• Creating AI applications using MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through custom operators and tiling techniques.
• Deploying models to either edge or cloud environments.

Format of the Course

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

Course Customization Options

• To arrange a customized training session tailored to your specific infrastructure or datasets, please get in touch with us.

CANN (Compute Architecture for Neural Networks) represents Huawei's dedicated AI compute stack, designed for deploying and optimizing AI models on Ascend AI processors.

This instructor-led live training, available either online or onsite, is tailored for intermediate-level AI developers and engineers aiming to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum leverages the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

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

• Grasping the CANN architecture and its critical function within the AI deployment pipeline.
• Converting and adapting models from widely-used frameworks into formats compatible with Ascend.
• Utilizing tools like ATC, OM model conversion, and MindSpore for both cloud and edge inference tasks.
• Identifying deployment issues and optimizing performance on Ascend hardware.

Course Format

• Interactive lectures coupled with live demonstrations.
• Practical lab sessions employing CANN tools along with Ascend simulators or physical devices.
• Real-world AI model deployment scenarios.

Customization Options for the Course

• For inquiries regarding customized training arrangements for this course, please contact us directly.

This instructor-led, live training in Serbia (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 Serbia (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers who wish to establish robust security protocols and resilience strategies for Edge AI systems.

Upon completing this training, participants will be capable of:

• Identifying security risks and vulnerabilities associated with Edge AI deployments.
• Deploying encryption and authentication methods to safeguard data.
• Architecting resilient Edge AI systems capable of withstanding cyber attacks.
• Executing secure AI model deployment strategies within edge environments.

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

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

Upon completing this training, participants will be equipped to:

• Configure and set up the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models 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, hands-on experience with 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) is Huawei’s comprehensive AI computing toolkit designed to compile, optimize, and deploy artificial intelligence models on Ascend AI processors.

This instructor-led live training, available both online and onsite, is tailored for beginner-level AI developers seeking to understand how CANN integrates into the model lifecycle—from initial training to final deployment—and how it interfaces with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Grasp the purpose and architectural design 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

• Interactive lectures accompanied by group discussions.
• Hands-on laboratory sessions featuring simple model deployment exercises.
• A step-by-step walkthrough of the CANN toolchain and its integration points.

Course Customization Options

• For organizations interested in a customized training program, please reach out to us to make the necessary arrangements.

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

This instructor-led training session, available both online and on-site, is designed for technical professionals ranging from beginner to intermediate levels. It aims to clarify how CANN and MindSpore collaborate to support AI lifecycle management and inform infrastructure decisions.

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

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

Course Format

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

Course Customization Options

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

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 deployed neural networks on Ascend AI processors.

This instructor-led, live training session, available online or on-site, targets advanced AI developers and system engineers looking to enhance inference performance through CANN's sophisticated toolset, such as the Graph Engine, TIK, and custom operator development.

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

• Comprehending CANN's runtime architecture and performance lifecycle.
• Utilizing profiling tools and the Graph Engine for performance analysis and optimization.
• Developing and optimizing custom operators using TIK and TVM.
• Addressing memory bottlenecks and enhancing 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.

Customization Options

• To arrange a customized training session for this course, please contact us directly.

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 on Huawei Ascend hardware.

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

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

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilize 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 tailored for edge or cloud-based deployment.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP use cases.

Customization Options

• For customized training requests, please reach out to us to arrange.

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 on-site) is designed for advanced-level system developers who want to create, 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 capable of:

• Writing and testing custom AI operators with 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 custom computation patterns.

Course Format

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

Course Customization Options

• To request customized training for this course, 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, conducted in Serbia (either online or on-site), targets 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 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.