CANN for Edge AI Deployment Training Course

This instructor-led, live training in the UAE (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.

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

Designed for intermediate-level IT architects, this instructor-led live training (available online or onsite) enables participants to understand and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be equipped to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Architect distributed systems optimized for ultra-low latency, high bandwidth, and autonomous operations.
• Incorporate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures prepared for 6G deployment.
• Assess the business and operational models facilitated by the integration of 6G and edge technologies.

Course Format

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

Customization Options

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

This instructor-led, live training in the UAE (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 represents a suite of AI processors engineered to deliver high-performance capabilities for both inference and training tasks.

This instructor-led live training session, available either online or onsite, is designed for AI engineers and data scientists at an intermediate level who aim to develop and refine neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.

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

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

Course Delivery Format

• Engaging lectures paired with interactive discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Guided exercises targeting model construction, training, and deployment.

Customization Opportunities

• For tailored training based on your specific infrastructure or datasets, please reach out to us to make arrangements.

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

This instructor-led, live training session, available both online and onsite, targets intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum utilizes the CANN toolkit alongside popular frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehend the CANN architecture and its critical function within the AI deployment pipeline.
• Convert and adapt models from leading frameworks into formats compatible with Ascend devices.
• Leverage tools such as ATC, OM model conversion, and MindSpore for both edge and cloud inference applications.
• Identify deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical lab exercises utilizing CANN tools, Ascend simulators, or physical devices.
• Real-world deployment scenarios based on existing AI models.

Customization Options

• For personalized training requirements, please contact us to arrange a customized session.

This instructor-led, live training in the UAE (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 the UAE (online or on-site) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand the security risks and vulnerabilities associated with Edge AI deployments.
• Implement encryption and authentication techniques to protect data.
• 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 chips designed to optimize both inference and training tasks in edge computing and data center environments.

This instructor-led live training, available online or onsite, is tailored for intermediate-level 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 able to:

• Set up and configure development environments for both BANGPy and Neuware.
• Develop and optimize Python- and C++-based models tailored for Cambricon MLUs.
• Deploy models to edge and data center devices operating on the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLU.

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

• To arrange customized training for this course based on your specific Cambricon device model or use case, please contact us.

CANN (Compute Architecture for Neural Networks) serves as 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 in online or onsite formats, is tailored for beginner-level AI developers. It provides a clear understanding of how CANN integrates into the end-to-end model lifecycle—from training through to deployment—and demonstrates its compatibility with major frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be equipped to:

• Grasp the fundamental purpose and architectural design of the CANN toolkit.
• Configure a development environment utilizing both CANN and MindSpore.
• Successfully convert and deploy a basic AI model onto Ascend hardware.
• Acquire the foundational knowledge necessary for future initiatives involving CANN optimization or integration.

Course Format

• Engaging lectures combined with interactive discussions.
• Practical, hands-on labs focused on simple model deployment.
• Detailed, step-by-step walkthroughs of the CANN toolchain and key integration points.

Customization Options

• For organizations seeking tailored training solutions, please reach out to us to arrange a customized session.

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

This instructor-led training session, available online or onsite, is designed for technical professionals ranging from beginner to intermediate levels who aim to grasp the synergy between CANN and MindSpore components to inform AI lifecycle management and infrastructure strategies.

Upon completing this course, participants will be equipped 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 its toolchain in comparison to industry standards.
• Strategically position Huawei's AI stack within enterprise or cloud/on-premises settings.

Course Format

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

Customization Options

• To arrange a tailored training session for this course, please reach out to us.

The 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 tailored for advanced AI developers and system engineers aiming to boost inference performance through CANN’s sophisticated toolset. Key areas include the Graph Engine, TIK, and custom operator development.

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

• Comprehend the runtime architecture and performance lifecycle within CANN.
• Leverage profiling tools and the Graph Engine for detailed performance analysis and optimization.
• Develop and refine custom operators utilizing TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Course Format

• Interactive lectures and group discussions.
• Practical labs featuring real-time profiling and operator tuning.
• Optimization exercises grounded in real-world edge-case deployment scenarios.

Customization Options

• For organizations seeking tailored training for this course, please contact us to arrange specifics.

The CANN SDK (Compute Architecture for Neural Networks) equips developers with robust deployment and optimization tools for real-time AI applications in computer vision and NLP, specifically tailored for Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate-level AI practitioners aiming to build, deploy, and optimize vision and language models via the CANN SDK for production environments.

Upon completion, participants will be capable of:

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Employing CANN utilities to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Course Format

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

Customization Options

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

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

This instructor-led, live training, available both online and onsite, is designed for advanced system developers aiming to build, deploy, and fine-tune custom operators for AI models. The curriculum leverages CANN’s TIK programming model alongside TVM compiler integration.

Upon completing this training, participants will be equipped to:

• Develop and test custom AI operators utilizing the TIK DSL for Ascend processors.
• Seamlessly integrate custom operators into the CANN runtime and execution graphs.
• Apply TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Debug and optimize instruction-level performance across various custom computation patterns.

Course Format

• Engaging lectures combined with practical demonstrations.
• Practical coding exercises for operators using TIK and TVM pipelines.
• Testing and tuning activities performed on Ascend hardware or emulators.

Course Customization Options

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

This instructor-led live training in the UAE (online or onsite) is designed for beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers looking 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 systems for monitoring crops and livestock.
• Create automated irrigation and environmental sensing solutions.
• Enhance agricultural efficiency through real-time Edge AI analytics.

This instructor-led live training in the UAE (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who aim 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.