CANN for Edge AI Deployment Training Course

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen on understanding 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 tailored 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 an innovative course that investigates the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

This instructor-led live training (available online or onsite) targets intermediate-level IT architects looking to master and design next-generation distributed architectures that leverage the combined strengths of 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Grasp how 6G will revolutionize edge computing and IoT architectures.
• Develop distributed systems for autonomous operations, high bandwidth, and ultra-low latency.
• Implement AI and data analytics at the edge to enable intelligent decision-making.
• Architect scalable, secure, and resilient edge infrastructures prepared for 6G.
• Assess the business and operational models facilitated by the convergence of 6G and edge computing.

Course Format

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

Course Customization Options

• For customized training arrangements, please contact us.

This instructor-led, live training in Norway (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 family of AI processors engineered for high-performance inference and training.

This instructor-led, live training (available online or onsite) targets intermediate-level AI engineers and data scientists who aim to develop and optimize neural network models utilizing 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 using 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 in sample applications.
• Guided exercises centered on model construction, training, and deployment.

Customization Options

• To request a customized version of this course tailored to your specific infrastructure or datasets, please contact us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as Huawei's dedicated AI compute stack, designed for deploying and optimizing artificial intelligence models on Ascend AI processors.

This instructor-led live training, available in both online and onsite formats, is tailored for intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum leverages the CANN toolkit alongside popular frameworks such as MindSpore, TensorFlow, or PyTorch.

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

• Grasp the CANN architecture and its pivotal role within the AI deployment pipeline.
• Convert and adapt models from widely used frameworks into Ascend-compatible formats.
• Utilize tools such as ATC, OM model conversion, and MindSpore to facilitate edge and cloud inference.
• Identify deployment challenges and optimize performance metrics on Ascend hardware.

Format of the Course

• Interactive lectures combined with live demonstrations.
• Practical lab exercises using CANN tools, along with Ascend simulators or physical devices.
• Real-world deployment scenarios grounded in practical AI models.

Course Customization Options

• For tailored training arrangements, please contact us to discuss your specific needs.

This live, instructor-led training in Norway (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts looking to gain practical skills in deploying AI models on edge devices for various applications.

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

• Grasp the core principles of Edge AI and its advantages.
• Establish and configure the necessary edge computing infrastructure.
• Create, train, and refine AI models tailored for edge deployment.
• Deploy practical AI solutions onto edge hardware.
• Assess and enhance the performance of models running on the edge.
• Navigate ethical and security aspects inherent to Edge AI applications.

This instructor-led, live training in Norway (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers seeking 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 associated with Edge AI deployments.
• Applying encryption and authentication techniques to protect data.
• Architecting resilient Edge AI systems capable of withstanding cyber threats.
• Executing secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI accelerators designed to optimize both inference and training tasks in edge computing and data center environments.

This instructor-led, live training session (available online or on-site) targets intermediate-level developers who aim to construct and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Establish and configure development environments for BANGPy and Neuware.
• Construct and optimize Python and C++ based models for Cambricon MLUs.
• Deploy models to edge and data center devices operating with the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

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

Course Customization Options

• To arrange customized training tailored to your specific Cambricon device model or use case, please contact us.

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

This instructor-led, live training (online or onsite) is aimed at 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 optimization 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 Customization Options

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

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

This instructor-led, live training (available online or on-site) targets beginner to intermediate technical professionals seeking to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decision-making.

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

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN facilitates model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and its toolchain in the context of industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-premise 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 Customization Options

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

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 either online or on-site, is designed for advanced AI developers and system engineers who aim to enhance inference performance by leveraging CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and capabilities for custom operator development.

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

• Comprehend the runtime architecture and performance lifecycle of CANN.
• 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 labs featuring real-time profiling and operator tuning.
• Optimization exercises based on edge-case deployment scenarios.

Customization Options

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

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, particularly when leveraging Huawei Ascend hardware.

This instructor-led, live training session (available online or onsite) targets intermediate-level AI professionals aiming to build, deploy, and optimize vision and language models using the CANN SDK for practical production scenarios.

Upon completing this course, participants will be capable of:

• Deploying and optimizing CV and NLP models using CANN and AscendCL.
• Utilizing CANN tools to convert models and seamlessly integrate them into operational pipelines.
• Enhancing inference performance for applications such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment environments.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on laboratory exercises focusing on model deployment and performance profiling.
• Real-time pipeline design utilizing actual CV and NLP use cases.

Customization Options

• For customized training arrangements for this course, please contact us to discuss your needs.

CANN TIK (Tensor Instruction Kernel) alongside Apache TVM facilitates the advanced optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) targets advanced-level system developers aiming to create, deploy, and tune custom operators for AI models utilizing 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 using the TIK DSL for Ascend processors.
• Integrate custom operators into the CANN runtime and execution graph.
• Leverage TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Course Format

• Interactive lectures and demonstrations.
• Hands-on coding exercises using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

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

This instructor-led, live training in Norway (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.

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

• Understand the role of Edge AI in precision agriculture.
• Implement AI-driven crop and livestock monitoring systems.
• Develop automated irrigation and environmental sensing solutions.
• Optimize agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training in Norway (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.