CANN for Edge AI Deployment Training Course

This instructor-led, live training in Botswana (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to investigate how 5G networks accelerate Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimised for low-latency applications within 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimise AI workloads for 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 Botswana (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 comprises a series of AI processors engineered for high-performance inference and training tasks.

This instructor-led live training, available online or on-site, targets intermediate-level AI engineers and data scientists keen on developing and optimizing neural network models via Huawei's Ascend platform and the CANN toolkit.

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

• Setting up and configuring the CANN development environment.
• Creating AI applications employing MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through custom operators and tiling techniques.
• Deploying models to either edge or cloud environments.

Course Format

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

Course Customization Options

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to make arrangements.

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

This live training session, led by an instructor and available either online or onsite, targets intermediate-level AI developers and engineers. It focuses on efficiently deploying trained AI models onto Huawei Ascend hardware by leveraging the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on laboratory exercises using CANN tools and Ascend simulators or physical devices.
• Practical deployment scenarios grounded in real-world AI models.

Course Customisation Options

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

This instructor-led live training in Botswana (online or on-site) 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 Botswana (online or onsite) targets advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems.

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

• Identifying security risks and vulnerabilities inherent in Edge AI deployments.
• Implementing encryption and authentication techniques to protect data.
• Designing resilient Edge AI architectures capable of withstanding cyber threats.
• Applying secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are purpose-built AI chips engineered for optimal inference and training performance in both edge computing and data centre environments.

This instructor-led, live training session (available online or at your premises) is designed for intermediate developers who want to build 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 the BANGPy and Neuware development environments.
• Develop and optimise Python- and C++-based models tailored for Cambricon MLUs.
• Deploy models onto edge and data centre devices operating with the Neuware runtime.
• Integrate machine learning workflows with hardware-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on experience using BANGPy and Neuware for development and deployment.
• Guided exercises concentrating on optimization, integration, and testing.

Customisation Options

• For a training programme 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 AI computing toolkit 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 wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimisation or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customisation Options

• To request a customised 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 closely integrated environment for AI development and deployment, specifically optimised for Ascend hardware.

This instructor-led, live training (available online or on-site) targets beginner to intermediate-level technical professionals who wish to grasp how the CANN and MindSpore components collaborate to facilitate AI lifecycle management and infrastructure decision-making.

Upon completing this training, participants will be equipped to:

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

Course Customisation Options

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

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

This instructor-led live training session, available online or onsite, is designed for advanced AI developers and system engineers who aim to optimise inference performance through CANN’s sophisticated toolkit, 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 detailed 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.
• Practical hands-on labs featuring real-time profiling and operator tuning.
• Optimisation exercises based on edge-case deployment scenarios.

Course Customisation Options

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

The CANN SDK (Compute Architecture for Neural Networks) offers robust 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 who want to build, deploy, and optimize vision and language models for production environments using the CANN SDK.

By the conclusion 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 integrate them into live pipelines.
• Optimize inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines for both edge and cloud-based deployment scenarios.

Course Format

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

Course Customization Options

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

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate the advanced optimisation and customisation of AI model operators for Huawei Ascend hardware.

This instructor-led, live training session (available online or onsite) is designed for advanced-level system developers who wish to construct, 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 capable of:

• Writing and testing custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computation patterns.

Course Format

• Interactive lectures and demonstrations.
• Hands-on coding of operators utilizing TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

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

This instructor-led, live training in Botswana (online or onsite) targets beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers eager to develop and deploy Edge AI solutions for smart farming.

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

• Grasp the significance 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 Botswana (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.