CANN for Edge AI Deployment Training Course

This instructor-led, live training in South Africa (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen on 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 ensure efficient performance on edge devices.

The '6G and the Intelligent Edge' course offers a forward-looking exploration into the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-driven data processing. This integration is designed to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training is available both online and onsite. It is tailored for intermediate-level IT architects seeking to comprehend and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be equipped to:

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

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 customized training for this course, please contact us to make arrangements.

This instructor-led, live training in South Africa (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 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 refers to a range of AI processors engineered for superior training and inference performance.

This guided, live training session (available online or in-person) is designed for mid-level data scientists and AI engineers looking to build and optimise neural network models leveraging the CANN toolkit and Huawei’s Ascend platform.

Upon completing this course, learners will be capable of:

• Configuring and setting up the CANN development environment.
• Creating AI applications using CloudMatrix workflows and MindSpore.
• Enhancing Ascend NPU performance via tiling and custom operators.
• Deploying models into cloud or edge settings.

Course Format

• Interactive discussions and lectures.
• Practical application of the CANN toolkit and Huawei Ascend in sample projects.
• Directed exercises concentrating on model building, deployment, and training.

Customization Opportunities

• For bespoke training aligned with your specific infrastructure or datasets, kindly reach out to us to arrange a session.

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

This instructor-led, live training—available either online or onsite—is tailored for intermediate-level AI developers and engineers who wish to efficiently deploy trained AI models onto Huawei Ascend hardware. Participants will utilize the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

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

• Grasp the CANN architecture and its pivotal role within the AI deployment pipeline.
• Convert and adapt models from popular frameworks into formats compatible with Ascend.
• Employ tools such as ATC, OM model conversion, and MindSpore for inference across both edge and cloud environments.
• Diagnose deployment challenges and optimise performance on Ascend hardware.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on laboratory sessions utilising CANN tools, Ascend simulators, or actual devices.
• Practical deployment scenarios based on real-world AI models.

Course Customisation Options

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

This instructor-led, live training in South Africa (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who wish to acquire practical skills in deploying AI models on edge devices for a variety of applications.

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

• Grasp the principles of Edge AI and its associated benefits.
• Set up and configure an edge computing environment.
• Develop, train, and optimise AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and enhance the performance of models deployed on the edge.
• Address ethical and security considerations in Edge AI applications.

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

Upon completing this training, participants will be able to:

• Identify security risks and vulnerabilities within Edge AI deployments.
• Implement encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure strategies for deploying AI models in edge environments.

Cambricon MLUs (Machine Learning Units) are purpose-built AI processors designed to enhance inference and training capabilities in both edge computing and data centre environments.

This instructor-led live training, available either online or onsite, is tailored for intermediate developers looking to construct and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be equipped to:

• Configure and set up development environments for BANGPy and Neuware.
• Develop and refine Python and C++ models optimised for Cambricon MLUs.
• Deploy models onto edge and data centre devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Practical application of BANGPy and Neuware for development and deployment.
• Guided exercises centred on optimization, integration, and testing.

Customisation Options

• To arrange bespoke training for this course, tailored to your specific Cambricon device model or use case, please contact us directly.

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

This instructor-led, live training (available online or onsite) targets beginner-level AI developers who wish to understand how CANN integrates into the model lifecycle from training to deployment, and how it interacts with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Grasp the purpose and architecture of the CANN toolkit.
• Set up a development environment featuring CANN and MindSpore.
• Convert and deploy a basic AI model to Ascend hardware.
• Acquire foundational knowledge to support future CANN optimisation or integration projects.

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 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 — delivers a tightly integrated AI development and deployment environment, specifically optimised for Ascend hardware.

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

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

• Grasp 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 relation to industry alternatives.
• Position Huawei’s AI stack within enterprise or cloud/on-premises environments.

Format of the Course

• 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 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 optimise the performance of neural networks deployed on Ascend AI processors.

This instructor-led training session (available online or onsite) targets advanced AI developers and system engineers looking to enhance inference performance through CANN’s sophisticated toolset, including the Graph Engine, TIK, and custom operator development.

Upon completion of this training, participants will be able 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 boost model throughput.

Course Format

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

Course Customisation Options

• To request a customised version of this course, please contact us to arrange your requirements.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimisation tools designed 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) targets intermediate-level AI professionals seeking to build, deploy, and optimise vision and language models using the CANN SDK for production environments.

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

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

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design using practical CV and NLP use cases.

Course Customization Options

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

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 instructor-led live training, available both online and on-site, targets advanced system developers keen on creating, deploying, and fine-tuning custom operators for AI models. The curriculum focuses on utilizing CANN's TIK programming model and integrating TVM compilers.

Upon completing this training, participants will be equipped to:

• Develop and test bespoke AI operators using the TIK DSL for Ascend processors.
• Seamlessly integrate custom operators into the CANN runtime and execution graph.
• Employ TVM for operator scheduling, auto-tuning, and performance benchmarking.
• Diagnose and optimize instruction-level performance for specific computation patterns.

Course Format

• Interactive lectures paired with live demonstrations.
• Practical coding exercises involving TIK and TVM pipelines.
• Hands-on testing and tuning performed on Ascend hardware or simulators.

Customization Options

• For personalized training arrangements, please get in touch with us.

This instructor-led, live training in South Africa (online or onsite) is designed for agritech professionals, IoT specialists, and AI engineers at beginner to intermediate levels who aim 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.
• 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 South Africa (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.