CANN for Edge AI Deployment Training Course

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists eager to discover how 5G networks accelerate 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 within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure 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 Nigeria (online or onsite) targets advanced-level AI practitioners, researchers, and developers seeking 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 range of AI processors engineered for superior performance in both inference and training tasks.

This live, instructor-led training session (available online or at your location) targets intermediate-level data scientists and AI engineers who aim to create and enhance neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.

Upon completing 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 tiling and custom operators.
• Deploy models across cloud or edge environments.

Course Format

• Interactive discussions and lectures.
• Practical application of the CANN toolkit and Huawei Ascend in sample projects.
• Guided exercises centred on model construction, training, and deployment.

Customization Options

• For tailored training aligned with your specific datasets or infrastructure, please get in touch with us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as Huawei’s AI compute stack, designed to deploy and optimise AI models on Ascend AI processors.

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

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

• Grasp the CANN architecture and its significance within the AI deployment pipeline.
• Convert and adapt models from popular frameworks into formats compatible with Ascend.
• Utilise tools such as ATC, OM model conversion, and MindSpore for inference on edge and cloud environments.
• Identify deployment issues and optimise performance on Ascend hardware.

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on lab sessions using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

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

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

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Execute encryption and authentication techniques to safeguard data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure AI model deployment strategies within edge environments.

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

This instructor-led live training, available either online or onsite, targets intermediate developers who want 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 the development environments for 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, hands-on development and deployment using BANGPy and Neuware.
• Guided exercises emphasizing optimization, integration, and testing.

Customization Options

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

CANN (Compute Architecture for Neural Networks) is Huawei’s toolkit for AI computing, designed to compile, optimise, and deploy AI models on Ascend AI processors.

This instructor-led, live training (available online or onsite) is tailored for beginner-level AI developers who want to grasp how CANN fits into the model lifecycle—from training to deployment—and how it integrates 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.
• Establish a development environment featuring CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration initiatives.

Format of the Course

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

Course Customization Options

• To arrange customized training for this course, please contact us to make the necessary arrangements.

Huawei’s AI stack—spanning from the low-level CANN SDK to the high-level MindSpore framework—delivers a tightly integrated environment for AI development and deployment, specifically optimised for Ascend hardware.

This instructor-led, live training (available online or onsite) is designed for beginner to intermediate technical professionals seeking to grasp how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.

Upon completing this training, participants will be able to:

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN facilitates model optimisation and hardware-level deployment.
• Evaluate the MindSpore framework and toolchain in relation 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 focusing on the model flow from MindSpore to CANN.

Course Customisation Options

• For bespoke training requests, please contact us to arrange accordingly.

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 neural networks deployed on Ascend AI processors.

This instructor-led live training, available both online and onsite, targets advanced AI developers and system engineers looking 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 equipped to:

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

Course Format

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

Customization Options

• To request a tailored training program for this course, please reach out to us to make arrangements.

The CANN SDK (Compute Architecture for Neural Networks) equips users with robust deployment and optimization tools 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 using CANN and AscendCL.
• Utilize CANN utilities to convert models and integrate them into live pipelines.
• Optimize 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 accompanied by demonstrations.
• Hands-on labs featuring model deployment and performance profiling.
• Live pipeline design utilizing real-world CV and NLP use cases.

Customization Options

• To arrange customized training for this course, please get in touch with us.

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

This guided, live training session (available online or in-person) is designed for experienced system developers aiming to create, implement, and refine custom operators for AI models utilizing CANN’s TIK programming framework and TVM compiler integration.

Upon completing this training, participants will be equipped to:

• Craft and evaluate custom AI operators employing the TIK DSL for Ascend processors.
• Seamlessly embed custom operators into the CANN runtime and execution graphs.
• Utilize TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Troubleshoot and enhance instruction-level performance for unique computational patterns.

Course Structure

• Engaging lectures paired with live demonstrations.
• Practical coding exercises involving operator development using TIK and TVM pipelines.
• Testing and tuning operations on Ascend hardware or simulated environments.

Customization Opportunities

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

This instructor-led, live training in Nigeria (available online or on-site) targets beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers who aim to develop and deploy Edge AI solutions for smart farming.

Upon completing this training, participants will be equipped 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 Nigeria (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.