CANN for Edge AI Deployment Training Course

This instructor-led, live training in Australia (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.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimised for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimise 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:

• 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 Australia (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimise their AI models for edge deployment, and explore specialised applications across various industries.

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

• Explore advanced techniques in Edge AI model development and optimisation.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilise specialised tools and frameworks for advanced Edge AI applications.
• Optimise the performance and efficiency of Edge AI solutions.
• Investigate 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.
• Optimise 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 Customisation Options

• To request a customised 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 compute stack, designed for the deployment and optimisation of AI models on Ascend AI processors.

This instructor-led, live training (delivered online or onsite) is tailored for intermediate-level AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing 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 Ascend-compatible formats.
• Utilise tools such as ATC, OM model conversion, and MindSpore for edge and cloud inference.
• Diagnose deployment challenges and optimise performance on Ascend hardware.

Course Format

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

Course Customisation Options

• To request customised training for this course, please get in touch with us to arrange.

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

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Implement encryption and authentication techniques to protect 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 dedicated AI chips engineered for high-performance inference and training in both edge and data centre environments.

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

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

• Set up and configure the BANGPy and Neuware development environments.
• Develop and optimise Python- and C++-based models specifically for Cambricon MLUs.
• Deploy models to edge and data centre 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 focusing on optimisation, integration, and testing.

Customisation Options

• To request tailored training for this course based on 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, optimise, and deploy AI models across Ascend AI processors.

This instructor-led live training, available both online and onsite, targets beginner-level AI developers keen 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 completion of this training, participants will be able to:

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment utilising CANN and MindSpore.
• Convert and deploy a simple AI model onto Ascend hardware.
• Build foundational knowledge for future CANN optimisation 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 integration points.

Customisation Options

• To arrange a tailored training session for this course, please get in touch with us.

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 optimised for Ascend hardware.

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

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

• Comprehend the layered architecture of Huawei’s AI compute stack.
• Identify how CANN facilitates model optimisation 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 Customisation Options

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

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

This instructor-led live training (available online or onsite) is designed for advanced AI developers and system engineers looking to enhance inference performance using CANN’s advanced toolset, including the Graph Engine, TIK, and custom operator development capabilities.

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

• Gain a clear understanding of CANN’s runtime architecture and performance lifecycle.
• Utilise profiling tools and the Graph Engine for performance analysis and optimisation.
• Create and optimise custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Format of the Course

• Interactive lectures and discussions.
• Hands-on labs featuring real-time profiling and operator tuning.
• Optimisation exercises using edge-case deployment examples.

Course Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) equips developers with powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, 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-grade use cases.

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

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilise CANN tools to convert models and seamlessly integrate them into live pipelines.
• Optimise inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab sessions focused on model deployment and performance profiling.
• Live pipeline design incorporating real-world CV and NLP use cases.

Course Customization Options

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

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

This instructor-led, live training session (available online or onsite) is designed for advanced system developers looking to create, deploy, and fine-tune custom operators for AI models using CANN's TIK programming model and TVM compiler integration.

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

• Write and test custom AI operators utilizing the TIK DSL for Ascend processors.
• Integrate bespoke operators into the CANN runtime and execution graph.
• Employ TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimise instruction-level performance for bespoke 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.

Customisation Options

• To request a tailored training version of this course, please contact us to make arrangements.

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

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

• Grasp the role of Edge AI in precision agriculture.
• Implement AI-driven monitoring systems for 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 Australia (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.