CANN for Edge AI Deployment Training Course

This instructor-led, live training in Brazil (online or in person) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks speed up Edge AI applications.

Upon completing 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 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 Brazil (online or onsite) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest Edge AI advancements, optimize their 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 for developing and optimizing Edge AI models.
• Implement leading-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Investigate innovative use cases and emerging trends in Edge AI.
• Navigate advanced ethical and security considerations in Edge AI deployments.

Huawei Ascend comprises a series of AI processors engineered for superior inference and training performance.

This instructor-led training session, available both online and onsite, targets intermediate AI engineers and data scientists aiming to create and refine neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.

Upon completion of this program, participants will gain the ability to:

• Establish and configure the CANN development environment.
• Construct AI applications through MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs via custom operators and tiling techniques.
• Deploy models to either edge or cloud settings.

Course Format

• Engaging lectures and discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Supervised exercises concentrating on model creation, training, and deployment.

Options for Course Customization

• For tailored training needs aligned with your specific infrastructure or datasets, please reach out to us to arrange.

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 training session, available both online and onsite, is tailored for intermediate-level AI developers and engineers aiming to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum utilizes the CANN toolkit alongside frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehend the CANN architecture and its significance within the AI deployment pipeline.
• Convert and adapt models from leading frameworks into formats compatible with Ascend.
• Leverage tools like ATC, OM model conversion, and MindSpore for inference tasks in both edge and cloud environments.
• Identify deployment challenges and optimize performance on Ascend hardware.

Course Format

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

Customization Options

• To arrange a customized training version of this course, please get in touch with us.

This instructor-led, live training in Brazil (online or in-person) is designed for intermediate-level developers, data scientists, and technology enthusiasts who want to acquire practical skills for deploying AI models on edge devices for various applications.

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

• Grasping the core principles of Edge AI and its advantages.
• Configuring and setting up the edge computing environment.
• Creating, training, and refining AI models for edge deployment.
• Executing practical AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed on the edge.
• Tackling ethical and security issues related to Edge AI applications.

This instructor-led, live training in Brazil (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers looking to implement strong security measures and resilience strategies for Edge AI systems.

Upon completing this training, participants will be able to:

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Deploy AI models securely within edge environments.

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

This instructor-led, live training session (available online or on-site) is designed for intermediate-level developers who want to build 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++-based models tailored for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

• Interactive lectures and discussions.
• Hands-on practice using BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Course 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 AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led live training, available both online and onsite, is tailored for beginner-level AI developers who want to grasp how CANN integrates into the model lifecycle, from training through deployment, and how it collaborates with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures and discussions.
• Practical labs focused on simple model deployment.
• Step-by-step walkthroughs of the CANN toolchain and its integration points.

Course Customization Options

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

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 live, instructor-led training (available online or onsite) targets beginner to intermediate technical professionals who want to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.

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

• Comprehend 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 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 covering the model flow from MindSpore to CANN.

Course Customization Options

• To request customized training 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 boost the performance of neural networks deployed on Ascend AI processors.

This instructor-led live training, available either online or on-site, is designed for advanced AI developers and system engineers aiming to enhance inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be able to:

• Grasp CANN’s runtime architecture and its performance lifecycle.
• Utilize profiling tools and the Graph Engine for performance analysis and optimization.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and increase model throughput.

Course Format

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

Customization Options

• To arrange a tailored version of this course, please contact us.

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

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

Upon completion 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 integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Real-time pipeline design using practical CV and NLP use cases.

Course Customization Options

• To request a customized training session for this course, please reach out to us for arrangement.

The combination of CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitates advanced optimization and customization of AI model operators specifically for Huawei Ascend hardware.

This instructor-led training session, available both online and in-person, targets advanced system developers looking to construct, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model and its integration with the TVM compiler.

Upon completing this training, participants will be equipped to:

• Develop and test custom AI operators by leveraging the TIK DSL for Ascend processors.
• Incorporate custom operators into the CANN runtime and execution graph.
• Apply TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Debug and enhance instruction-level performance for complex custom computation patterns.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical coding exercises for operators using TIK and TVM pipelines.
• Hands-on testing and tuning on Ascend hardware or in simulator environments.

Options for Course Customization

• To request a tailored version of this course, please get in touch with us to arrange the details.

This instructor-led, live training in Brazil (online or onsite) is designed for beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers seeking to develop and deploy 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.

This instructor-led, live training in Brazil (online or onsite) is designed for robotics engineers at an intermediate level, 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.