CANN for Edge AI Deployment Training Course

This instructor-led live training in Kenya (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 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:

• Grasp how 6G will revolutionize edge computing and IoT architectures.
• Architect distributed systems tailored for ultra-low latency, high bandwidth, and autonomous operations.
• Incorporate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Assess 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 Kenya (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 offers a family of AI processors engineered for high-performance inference and training tasks.

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

Upon completing this training, participants will be equipped to:

• Set up and configure the CANN development environment.
• Create AI applications leveraging MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the use of custom operators and tiling techniques.
• Deploy models into edge or cloud environments.

Course Format

• Interactive lectures coupled with group discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample applications.
• Guided exercises centered 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 arrange a session.

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

This instructor-led training session, available either online or onsite, is tailored for intermediate-level AI developers and engineers. Participants will learn how to efficiently deploy trained AI models onto Huawei Ascend hardware by utilizing the CANN toolkit alongside established frameworks such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will gain the ability to:

• Grasp the CANN architecture and its critical function within the AI deployment pipeline.
• Convert and adapt models from widely used frameworks into formats compatible with Ascend processors.
• Utilize utilities such as ATC, OM model conversion, and MindSpore for both edge and cloud-based inference tasks.
• Identify and resolve deployment challenges while optimizing performance on Ascend hardware.

Course Format

• Interactive lectures paired with live demonstrations.
• Practical laboratory exercises involving CANN tools and Ascend simulators or physical devices.
• Real-world deployment scenarios derived from actual AI models.

Customization Options

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

This instructor-led, live training in Kenya (online or onsite) is aimed at intermediate-level developers, data scientists, and technology 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 Kenya (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers aiming to establish strong security measures and resilience strategies within Edge AI systems.

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

• Identify security risks and vulnerabilities inherent in Edge AI deployments.
• Deploy encryption and authentication methods to protect data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Execute secure AI model deployment strategies tailored for edge environments.

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

This instructor-led live training, available online or on-site, targets intermediate developers looking to build and deploy AI models using 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 environments for BANGPy and Neuware.
• Develop and optimize Python and C++ based models for Cambricon MLUs.
• Deploy models to edge and data center devices running the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Hands-on practice with BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Customization Options

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

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 either online or onsite, is tailored for beginner-level AI developers. The course aims to help participants understand how CANN integrates into the model lifecycle, from training through to deployment, and how it interacts with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

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

Course Format

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

Customization Options

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

Huawei’s AI stack spans from the foundational CANN SDK to the advanced MindSpore framework, providing a seamlessly integrated environment for developing and deploying AI solutions optimized for Ascend hardware.

This instructor-led live training, available online or onsite, is designed for technical professionals at beginner to intermediate levels who aim to comprehend how CANN and MindSpore collaborate to manage the AI lifecycle and inform infrastructure strategies.

Upon completing this training, participants will be equipped 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 against industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-premises environments.

Course Format

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

Course Customization Options

• For customized training requests, please get in touch to arrange your session.

CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute layer, 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 onsite, targets advanced AI developers and system engineers looking to boost inference performance through CANN’s sophisticated toolset. Key areas of focus include the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be equipped to:

• Grasp CANN's runtime architecture and its performance lifecycle.
• Utilize profiling tools and the Graph Engine for thorough performance analysis and optimization.
• Develop and optimize custom operators using 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 utilizing edge-case deployment scenarios.

Customization Options

• For personalized training on this course, please reach out to 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) targets intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completing this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Develop real-time CV/NLP pipelines for both edge and cloud-based deployment scenarios.

Course Format

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

Customization Options

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

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) targets advanced-level system developers looking to build, deploy, and tune custom operators for AI models by leveraging CANN’s TIK programming model and TVM compiler integration.

Upon completing 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.

Course Format

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

Customization Options

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

This instructor-led, live training in Kenya (online or onsite) is designed for beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers interested in developing and deploying Edge AI solutions for smart farming.

By the conclusion 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 live, instructor-led training delivered in Kenya (online or on-site) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers seeking 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.