CANN for Edge AI Deployment Training Course

This instructor-led, live training in Norway (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish 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 delves into the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing. This combination supports intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available both online and onsite) is designed for intermediate-level IT architects who are interested in understanding and designing next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

• Understand how 6G will revolutionize edge computing and IoT architectures.
• Design distributed systems that ensure 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 6G-ready edge infrastructures.
• Evaluate business and operational models that are enabled by the convergence of 6G and edge technologies.

Format of the Course

• Interactive lectures and discussions.
• Case studies and practical architecture design exercises.
• Hands-on simulations 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 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 is a series of AI processors designed for efficient inference and high-performance training.

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

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

• Set up and configure the CANN development environment effectively.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs by utilizing custom operators and tiling techniques.
• Deploy models to both edge and cloud environments seamlessly.

Format of the Course

• Interactive lectures and discussions.
• Practical hands-on experience with Huawei Ascend and the CANN toolkit through sample applications.
• Guided exercises focused on building, training, and deploying models.

Course Customization Options

• For a customized training session tailored to your specific infrastructure or datasets, please contact us to arrange.

CANN (Compute Architecture for Neural Networks) is 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) is tailored for intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models to Huawei Ascend hardware using the CANN toolkit and frameworks such as MindSpore, TensorFlow, or PyTorch.

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

• Grasp the CANN architecture and its significance in the AI deployment pipeline.
• Transform models from popular frameworks into Ascend-compatible formats.
• Utilize tools like ATC, OM model conversion, and MindSpore for edge and cloud inference.
• Identify and resolve deployment issues while optimizing performance on Ascend hardware.

Format of the Course

• Interactive lecture and demonstration sessions.
• Hands-on lab work using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

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 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 Norway (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for optimizing inference and training in both edge and datacenter environments.

This instructor-led, live training (available online or on-site) is targeted at intermediate-level developers who want 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 development environments for BANGPy and Neuware.
• Create and optimize Python- and C++-based models for use with Cambricon MLUs.
• Deploy these models to devices in edge and data center settings running the Neuware runtime.
• Integrate machine learning workflows with the specific acceleration features of MLUs.

Format of the Course

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

Course Customization Options

• To request a tailored training program based on your specific Cambricon device model or use case, please contact us to arrange.

CANN (Compute Architecture for Neural Networks) is 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) is targeted at beginner-level AI developers who want to understand how CANN integrates into the model lifecycle from training to deployment, and how it works 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 using CANN and MindSpore.
• Convert and deploy a simple AI model on Ascend hardware.
• Acquire foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion sessions.
• Hands-on labs focusing on simple model deployment.
• Step-by-step guidance through 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, ranging from the low-level CANN SDK to the high-level MindSpore framework, provides a seamlessly integrated environment optimized for AI development and deployment on Ascend hardware.

This instructor-led, live training (available both online and onsite) is designed for technical professionals at beginner to intermediate levels who want to gain an understanding of how CANN and MindSpore work together to support the entire AI lifecycle and inform infrastructure decisions.

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

• Grasp the layered architecture of Huawei’s AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and toolchain in comparison to industry alternatives.
• Integrate Huawei's AI stack into enterprise or cloud/on-prem environments effectively.

Format of the Course

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs on model flow from MindSpore to CANN.

Course Customization Options

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

CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI computing foundation that enables developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors.

This instructor-led, live training (online or onsite) is designed for advanced-level AI developers and system engineers who want to enhance inference performance using CANN’s sophisticated toolset, including the Graph Engine, TIK, and custom operator development.

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

• Comprehend CANN's runtime architecture and performance lifecycle.
• Leverage profiling tools and the Graph Engine for performance analysis and optimization.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Format of the Course

• Interactive lectures and discussions.
• Practical labs with real-time profiling and operator tuning.
• Optimization exercises using edge-case deployment scenarios.

Course Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led, live training (available both online and onsite) is designed for intermediate-level AI practitioners who aim to build, deploy, and optimize vision and language models using the CANN SDK for production scenarios.

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

• Deploy and optimize computer vision and natural language processing 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 computer vision and natural language processing pipelines suitable for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab sessions focusing on model deployment and performance profiling.
• Live pipeline design using practical computer vision and natural language processing use cases.

Course Customization Options

• For a customized training tailored to your specific needs, please contact us to arrange.

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate 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-level 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.

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

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom operations into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Format of the Course

• Interactive lecture and demonstration.
• 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 for this course, please contact us to arrange.

This instructor-led, live training in Norway (online or onsite) is aimed at beginner-level to intermediate-level 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.