CANN for Edge AI Deployment Training Course

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen to explore 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 tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimise AI workloads for efficient performance on edge devices.

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

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

After completing this course, participants will be able to:

• Grasp how 6G will transform edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G.
• Evaluate business and operational models enabled by the convergence of 6G and edge technologies.

Course Format

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

Customization Options

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

This instructor-led, live training in Malaysia (online or on-site) 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.

This instructor-led live training session, available online or on-site, is tailored for intermediate-level AI engineers and data scientists eager to develop and optimize neural network models using 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 utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the use of custom operators and tiling techniques.
• Deploy models across edge or cloud environments.

Course Format

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

Customization Options for the Course

• To arrange a customized training session tailored to your specific infrastructure or datasets, please get in touch with us.

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, is tailored for intermediate-level AI developers and engineers looking to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilises the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures complemented by demonstrations.
• Practical hands-on lab sessions using CANN tools alongside Ascend simulators or physical devices.
• Real-world deployment scenarios based on authentic AI models.

Course Customisation Options

• For a bespoke training experience, please reach out to us to arrange your requirements.

This instructor-led live training in Malaysia (online or onsite) is designed for intermediate-level developers, data scientists, and tech enthusiasts looking 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 Malaysia (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 artificial intelligence chips designed to optimize inference and training tasks for both edge computing and data center environments.

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

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

• Setting up and configuring development environments for BANGPy and Neuware.
• Creating and optimizing Python and C++ based models for Cambricon MLUs.
• Deploying models to edge and data center devices operating on the Neuware runtime.
• Integrating 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

• For a customized training session tailored to your specific Cambricon device model or use case, please contact us to make arrangements.

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

This instructor-led training session, available either online or at your office, targets beginner-level AI developers who want to grasp how CANN integrates into the model lifecycle—from training to deployment—and how it collaborates with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be equipped to:

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment utilising CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimisation or integration initiatives.

Course Format

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

Customisation Options

• For those seeking a tailored version of this course, please reach out to us to make arrangements.

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 optimised for Ascend hardware.

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

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

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN supports 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.

Format of the Course

• Interactive lecture and discussion.
• Live system demos and case-based walkthroughs.
• Optional guided labs on model flow from MindSpore to CANN.

Course Customization Options

• To request a 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, empowering developers to fine-tune and enhance the performance of deployed neural networks on Ascend AI processors.

This instructor-led live training, available either online or onsite, is designed for advanced AI developers and system engineers who aim to maximise inference performance by leveraging CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

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

• Comprehend CANN's runtime architecture and performance lifecycle.
• Utilise profiling tools and the Graph Engine for effective performance analysis and optimisation.
• Develop and optimise 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.
• Optimisation exercises incorporating edge-case deployment examples.

Customisation Options

• For tailored training arrangements for this course, please contact us directly.

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

This instructor-led live 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 environments.

Upon completion of 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 object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud deployment scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Hands-on lab sessions focused on model deployment and performance profiling.
• Live pipeline design exercises using practical CV and NLP use cases.

Course Customization Options

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

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 live training, available either online or onsite, is designed for advanced system developers who aim to construct, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model alongside TVM compiler integration.

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

• Compose and test custom AI operators by employing the TIK DSL for Ascend processors.
• Embed custom operators into the CANN runtime and execution graph.
• Leverage TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and enhance instruction-level performance for bespoke computation patterns.

Course Format

• Engaging lectures complemented by practical demonstrations.
• Practical coding exercises involving operators through TIK and TVM pipelines.
• Testing and tuning procedures performed on Ascend hardware or within simulators.

Customization Options for the Course

• For personalized training requirements, please reach out to us to make arrangements.

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

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

• Comprehend the role of Edge AI in precision agriculture.
• Implement AI-driven systems for monitoring crops and livestock.
• Develop automated irrigation and environmental sensing solutions.
• Enhance agricultural efficiency through real-time Edge AI analytics.

This instructor-led live training in Malaysia (online or onsite) targets 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.