CANN for Edge AI Deployment Training Course

This instructor-led, live training in Mexico (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:

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to achieve efficient performance on edge devices.

"6G and the Intelligent Edge" is a forward-looking course that delves into 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:

• Grasp 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 Mexico (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 family of AI processors designed for high-performance inference and training.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimize neural network models using Huawei’s Ascend platform and the CANN toolkit.

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

• Set up and configure the CANN development environment.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Optimize performance on Ascend NPUs using custom operators and tiling.
• Deploy models to edge or cloud environments.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Huawei Ascend and CANN toolkit in sample applications.
• Guided exercises focused on model building, training, and deployment.

Course Customization Options

• To request a customized training for this course based on your infrastructure or datasets, please contact us to arrange.

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

This instructor-led, live training (available online or onsite) targets intermediate-level AI developers and engineers who want to efficiently deploy trained AI models to Huawei Ascend hardware using the CANN toolkit and tools like MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures and live demonstrations.
• Practical lab sessions using CANN tools alongside Ascend simulators or devices.
• Real-world deployment scenarios featuring actual AI models.

Course Customization Options

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

This instructor-led, live training in Mexico (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 Mexico (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• 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 processors optimized for inference and training in both edge computing and data center environments.

This instructor-led, live training (available online or onsite) is designed for intermediate developers seeking to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Set up and configure 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 running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features.

Course Format

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

Customization Options

• To request customized training tailored to your specific Cambricon device model or use case, please contact us to arrange.

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 session, 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 to deployment—and how it interfaces with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be capable of:

• Understanding the purpose and architecture of the CANN toolkit.
• Configuring a development environment utilizing CANN and MindSpore.
• Converting and deploying a basic AI model onto Ascend hardware.
• Acquiring foundational knowledge to support future CANN optimization or integration initiatives.

Course Format

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

Customization Options

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

Huawei’s AI stack — ranging 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 training, available either online or on-site, is designed for technical professionals at beginner to intermediate levels who want to understand how the CANN and MindSpore components collaborate to manage the AI lifecycle and support 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.
• Evaluate 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 it.

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei's foundational AI compute platform, empowering developers to fine-tune and maximize the efficiency of neural networks deployed on Ascend AI processors.

This instructor-led training session, available either online or in-person, is designed for advanced AI developers and system engineers looking to boost 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 to analyze and optimize performance.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Course Format

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

Customization Options

• To arrange a tailored training version of this course, please reach out to us.

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

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

Upon completing this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize 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 suitable for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with 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 information on requesting customized training for this course, please get in touch with us to make arrangements.

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

This instructor-led, live training (available online or onsite) is designed for advanced system developers who want to build, deploy, and tune custom operators for AI models using TIK’s programming model and TVM’s compiler integration within the CANN ecosystem.

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

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom operators into the CANN runtime and execution graph.
• Leverage TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Course Format

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

Customization Options

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

This instructor-led, live training in Mexico (online or onsite) is designed for beginner to intermediate agritech professionals, IoT specialists, and AI engineers who want 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 Mexico (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.