CANN for Edge AI Deployment Training Course

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

Upon completion 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.

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 Romania (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 high-performance inference and training tasks.

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

Upon completing this training, participants will be capable of:

• Configuring and setting up the CANN development environment.
• Building AI applications leveraging MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs via custom operators and tiling techniques.
• Deploying models to either cloud or edge environments.

Course Format

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

Customization Options

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

CANN (Compute Architecture for Neural Networks) serves as Huawei’s AI computing stack, designed to facilitate the deployment and optimization of AI models on Ascend AI processors.

This instructor-led live training session, available either online or on-site, is designed for intermediate-level AI developers and engineers aiming to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum utilizes the CANN toolkit along with tools like MindSpore, TensorFlow, and PyTorch.

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

• Gain a comprehensive understanding of the CANN architecture and its critical role within the AI deployment pipeline.
• Convert and adapt models from leading frameworks into formats compatible with Ascend processors.
• Utilize tools such as ATC, OM model conversion utilities, and MindSpore for both edge and cloud inference tasks.
• Troubleshoot deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical lab exercises utilizing CANN tools and Ascend simulators or physical devices.
• Real-world AI model deployment scenarios for hands-on experience.

Course Customization Options

• For tailored training needs related to this course, please reach out to us to make arrangements.

This instructor-led, live training in Romania (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 Romania (online or onsite) 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.

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

• Identifying security risks and vulnerabilities within Edge AI deployments.
• Applying encryption and authentication methods to protect data.
• Designing resilient Edge AI architectures capable of withstanding cyber threats.
• Utilizing secure strategies for deploying AI models in edge environments.

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

This instructor-led live training (available online or onsite) is designed for intermediate-level developers who want to create and deploy AI models using 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 BANGPy and Neuware.
• Develop and optimize Python and C++ models for Cambricon MLUs.
• Deploy models to edge and data center devices utilizing the Neuware runtime.
• Integrate ML 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 request a customized training session tailored to your specific Cambricon device model or use case, please contact us to arrange.

CANN (Compute Architecture for Neural Networks) is Huawei's specialized AI computing toolkit designed to compile, optimize, and deploy artificial intelligence models on Ascend AI processors.

This instructor-led live training session, available either online or on-site, targets beginner-level AI developers. The course explores how CANN integrates into the entire model lifecycle—from training through to deployment—and demonstrates its compatibility with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be equipped to:

• Comprehend the purpose and architectural design of the CANN toolkit.
• Configure a development environment utilizing both CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational insights necessary for future projects involving CANN optimization or integration.

Course Format

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

Customization Options

• For tailored training sessions regarding 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, provides a tightly integrated environment for AI development and deployment, specifically optimized for Ascend hardware.

This instructor-led live training, available either online or onsite, targets technical professionals at beginner to intermediate levels who want to understand how 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 compute stack.
• Identify how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
• Contextualize 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.

Course Customization Options

• For customized training requests for this course, please contact us to arrange.

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

This instructor-led live training (available online or onsite) targets advanced AI developers and system engineers seeking to boost inference performance through CANN’s sophisticated toolkit, which includes the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be equipped to:

• Comprehend CANN’s runtime architecture and its performance lifecycle.
• Utilize profiling tools and the Graph Engine to analyze and enhance performance.
• Develop and optimize custom operators leveraging TIK and TVM.
• Address memory bottlenecks and elevate model throughput.

Course Format

• Interactive lectures accompanied by group discussions.
• Practical labs featuring real-time profiling and operator tuning.
• Optimization exercises based on deployment scenarios with edge-case constraints.

Customization Options

• For personalized training arrangements, please contact us.

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

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

By the conclusion 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 live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud deployment environments.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical labs focusing on model deployment and performance profiling.
• Live pipeline design exercises using real-world CV and NLP scenarios.

Course Customization Options

• For customized training requests, please contact us to arrange details.

Leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM facilitates the sophisticated optimization and customization of AI model operators tailored for Huawei Ascend hardware.

This instructor-led, live training session, available in both online and onsite formats, is designed for advanced system developers looking to construct, deploy, and fine-tune custom operators for AI models. The curriculum focuses on utilizing CANN’s TIK programming model in conjunction with TVM compiler integration.

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

• Author and validate custom AI operators employing the TIK DSL specifically for Ascend processors.
• Seamlessly integrate custom operators into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Debug and enhance instruction-level performance to address custom computation patterns.

Course Format

• Engaging lectures coupled with practical demonstrations.
• Practical coding exercises involving operators via TIK and TVM pipelines.
• Testing and optimization performed on Ascend hardware or simulated environments.

Course Customization Options

• For bespoke training arrangements, please reach out to us to coordinate your specific requirements.

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

Upon completing this training, participants will be able to:

• Grasp the significance 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 Romania (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.