CANN for Edge AI Deployment Training Course

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

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

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize 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 Slovakia (online or onsite) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest Edge AI advancements, optimize their models for edge deployment, and explore specialized applications across various industries.

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

• Investigate advanced techniques in Edge AI model development and optimization.
• Apply state-of-the-art strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Examine innovative use cases and emerging trends in Edge AI.
• Tackle advanced ethical and security challenges in Edge AI deployments.

Huawei Ascend comprises a series of AI processors engineered for high-efficiency inference and training tasks.

This instructor-led, live training (available online or on-site) targets intermediate-level AI engineers and data scientists seeking to create and optimize neural network models using Huawei’s Ascend platform alongside the CANN toolkit.

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

• Configuring and setting up the CANN development environment.
• Creating AI applications through MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs via custom operators and tiling techniques.
• Deploying models to both edge and cloud environments.

Course Format

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

Customization Options

• To arrange customized training tailored to your specific infrastructure or datasets, please reach out to us.

CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute stack, designed for the deployment and optimization of AI models on Ascend AI processors.

This instructor-led live training, available both online and onsite, targets intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models to Huawei Ascend hardware. The curriculum focuses on utilizing the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completion of 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 popular frameworks into Ascend-compatible formats.
• Utilize tools like ATC, OM model conversion, and MindSpore for inference on both edge and cloud environments.
• Troubleshoot deployment issues and optimize performance on Ascend hardware.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Slovakia (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 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 Slovakia (online or onsite) is designed for advanced-level 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 able to:

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips optimized for inference and training in edge and datacenter scenarios.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Set up and configure the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge and data center devices running Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimization, integration, and testing.

Course Customization Options

• To request a customized training for this course based on your 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, optimize, and deploy AI models on Ascend AI processors.

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

Upon completing this training, participants will be capable of:

• Comprehending the purpose and architecture of the CANN toolkit.
• Configuring a development environment utilizing CANN and MindSpore.
• Converting and deploying a basic AI model onto Ascend hardware.
• Acquiring foundational knowledge to support future CANN optimization or integration initiatives.

Course Format

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

Customization Options

• For customized training arrangements, please contact us.

Huawei’s AI infrastructure — spanning the low-level CANN SDK to the high-level MindSpore framework — delivers a seamlessly integrated environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.

This instructor-led, live training (available online or on-site) targets beginner to intermediate technical professionals aiming to understand how CANN and MindSpore collaborate to manage the AI lifecycle and inform infrastructure strategies.

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

• Grasp the layered architecture of Huawei’s AI computing stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolset in comparison to industry standards.
• Determine where Huawei’s AI stack fits within enterprise, cloud, or on-premises environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case study walkthroughs.
• Optional guided labs exploring the model flow from MindSpore to CANN.

Customization Options

• For a tailored version of this course, please contact us to arrange your requirements.

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, enabling developers to fine-tune and optimize the efficiency 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 seeking to boost inference performance using CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and capabilities for custom operator development.

Upon completing this training, participants will be equipped to:

• Comprehend CANN's runtime architecture and its performance lifecycle.
• Utilize profiling tools and the Graph Engine for detailed performance analysis and optimization.
• Develop and optimize custom operators employing TIK and TVM.
• Address memory bottlenecks and enhance 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

• For those requiring a tailored training experience, please reach out to us to make arrangements.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools designed 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 tailored for intermediate-level AI professionals looking to build, 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 integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training session (available online or in-person) is designed for advanced system developers who aim to build, deploy, and refine custom operators for AI models using CANN’s TIK programming model and TVM compiler integration.

Upon completion of this training, participants will be able 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.
• Practical coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Customization Options

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

This instructor-led, live training in Slovakia (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:

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

This instructor-led, live training in Slovakia (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.