CANN for Edge AI Deployment Training Course

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of 5G technology and its influence on Edge AI.
• Deploying AI models specifically optimized for low-latency requirements within 5G infrastructures.
• Building real-time decision-making systems that leverage Edge AI and 5G connectivity.
• Optimizing AI workloads to ensure efficient operation on edge devices.

6G and the Intelligent Edge is a forward-thinking course that investigates the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-powered data processing to enable intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available online or onsite) targets intermediate-level IT architects looking to understand and design next-generation distributed architectures by leveraging the synergy between 6G connectivity and intelligent edge systems.

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

• Grasp how 6G will reshape edge computing and IoT architectures.
• Design distributed systems supporting 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 infrastructures prepared for 6G.
• Evaluate 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 using 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 Czech Republic (online or onsite) targets advanced-level AI practitioners, researchers, and developers aiming to master recent advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across diverse industries.

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

• Investigate advanced techniques for developing and optimizing Edge AI models.
• Apply modern 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.
• Discover innovative use cases and emerging trends in Edge AI.
• Tackle advanced ethical and security challenges in Edge AI deployments.

Huawei Ascend offers a family of AI processors engineered for high-performance inference and training tasks.

This instructor-led live training (available online or onsite) is designed for intermediate-level AI engineers and data scientists looking to develop and optimize neural network models using Huawei’s Ascend platform and the CANN toolkit.

Upon completing this training, participants will be able to:

• Set up and configure the CANN development environment.
• Build AI applications leveraging MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy 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 focusing 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 arrange the details.

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

This instructor-led live training, available online or onsite, targets intermediate AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit alongside tools like 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 leading frameworks into formats compatible with Ascend.
• Utilize tools such as ATC, OM model conversion, and MindSpore for both cloud and edge inference.
• Identify deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures combined with demonstrations.
• Practical laboratory sessions using CANN tools with Ascend simulators or devices.
• Real-world AI model deployment scenarios.

Customization Options

• For customized training on this course, please contact us to make arrangements.

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

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

• Grasp the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to safeguard data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Deploy AI models securely within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize both inference and training processes in edge computing and datacenter environments.

This instructor-led live training session (available online or onsite) targets intermediate-level 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 able to:

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

Course Format

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

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 toolkit for AI computing, designed to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led live training, available online or onsite, is designed for beginner-level AI developers. It aims to help participants understand how CANN integrates into the model lifecycle, from training to deployment, and how it interacts with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be able to:

• Comprehend the purpose and architecture of the CANN toolkit.
• Configure a development environment using CANN and MindSpore.
• Convert and deploy a simple AI model on Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration projects.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training (available online or onsite) is designed for technical professionals at beginner to intermediate levels who want to understand how CANN and MindSpore components collaborate to support AI lifecycle management and make informed infrastructure decisions.

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

• Comprehend the layered architecture of Huawei's AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
• Position Huawei's AI stack within enterprise, cloud, or 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.

Customization Options

• For customized training on this course, please contact us to arrange.

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

This instructor-led training, available either online or onsite, is designed for senior AI developers and system engineers who aim to boost inference performance by leveraging CANN’s advanced tools, such as the Graph Engine, TIK, and custom operator development capabilities.

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

• Gaining a deep understanding of CANN's runtime architecture and its performance lifecycle.
• Utilizing profiling tools and the Graph Engine to analyze and enhance performance.
• Developing and optimizing custom operators using TIK and TVM.
• Addressing memory bottlenecks and increasing 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 personalized training arrangements, please contact us to discuss your requirements.

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 when leveraging Huawei Ascend hardware.

This guided, live training session (available online or on-site) targets AI professionals with intermediate skills who aim to develop, deploy, and optimize vision and language models using the CANN SDK for practical, production-level scenarios.

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

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Employing CANN utilities to transform models and incorporate them into operational pipelines.
• Enhancing inference performance for specific tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines tailored for edge or cloud-based deployment environments.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical laboratory sessions focusing on model deployment and performance profiling.
• Designing live pipelines based on real-world CV and NLP use cases.

Customization Options for the Course

• For inquiries regarding customized training for this course, please reach out to us to coordinate arrangements.

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 (available online or onsite) is designed for advanced system developers who want to build, deploy, and fine-tune custom operators for AI models using CANN's TIK programming model and TVM compiler integration.

Upon completing this training, participants will be able to:

• Write and test custom AI operators utilizing 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 of operators using 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 it.

This instructor-led, live training in Czech Republic (online or onsite) is designed for agritech professionals, IoT specialists, and AI engineers at the beginner to intermediate level who want 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 solutions for automated irrigation and environmental sensing.
• Enhance agricultural efficiency through real-time Edge AI analytics.

This instructor-led, live training in Czech Republic (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.