CANN for Edge AI Deployment Training Course

This instructor-led, live training in Argentina (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists eager to explore 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 optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

This forward-looking course investigates the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

Designed for intermediate-level IT architects, this live, instructor-led training (available online or onsite) helps participants understand and design next-generation distributed architectures that leverage the powerful synergy between 6G connectivity and intelligent edge systems.

By the end of this course, participants will be equipped to:

• Comprehend the transformative impact of 6G on edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G.
• Evaluate the business and operational models facilitated by the convergence of 6G and edge technologies.

Course Format

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

Customization Options

• For customized training arrangements, please contact us directly.

This instructor-led, live training in Argentina (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 suite of AI processors engineered for high-performance training and inference tasks.

This instructor-led live training session (available online or on-site) targets intermediate-level data scientists and AI engineers interested in developing and optimizing neural network models using Huawei's CANN toolkit and Ascend platform.

Upon completion, participants will be capable of:

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

Course Format

• Interactive discussions and lectures.
• Practical application of Huawei Ascend and the CANN toolkit in sample projects.
• Guided exercises centered on deployment, training, and model construction.

Customization Options

• For tailored training aligned with your specific infrastructure or datasets, please reach out to arrange a session.

CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute infrastructure designed for deploying and enhancing AI models on Ascend AI processors.

This guided, live training session (available online or at your location) targets intermediate AI developers and engineers seeking to efficiently deploy trained models onto Huawei Ascend hardware utilizing the CANN toolkit alongside tools like MindSpore, TensorFlow, or PyTorch.

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

• Grasp the CANN architecture and its function within the AI deployment workflow.
• Transform and adapt models from leading frameworks into Ascend-compatible formats.
• Leverage tools such as ATC, OM model conversion, and MindSpore for both edge and cloud inference.
• Identify deployment challenges and enhance performance on Ascend hardware.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on exercises using CANN tools with Ascend simulators or physical devices.
• Real-world deployment scenarios grounded in actual AI models.

Customization Options for the Course

• For customized training arrangements, please reach out to us to coordinate.

This live, instructor-led training in Argentina (online or on-site) is designed for intermediate developers, data scientists, and technology enthusiasts seeking to acquire practical expertise in deploying AI models on edge devices across diverse applications.

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

• Grasping the foundational principles of Edge AI and its advantages.
• Establishing and configuring the edge computing infrastructure.
• Creating, training, and refining AI models for edge deployment.
• Executing practical AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed at the edge.
• Navigating ethical and security challenges associated with Edge AI applications.

This instructor-led, live training in Argentina (online or onsite) is designed for advanced-level cybersecurity experts, AI engineers, and IoT developers seeking to implement strong security measures and resilience strategies for Edge AI systems.

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Deploy encryption and authentication methods to ensure data protection.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Apply secure strategies for deploying AI models in edge environments.

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

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

Upon completion 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 the Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Course Format

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

Course Customization Options

• To request a customized training session for this course 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 both online and onsite, targets beginner-level AI developers looking 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 to Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration projects.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on simple model deployment.
• Step-by-step walkthroughs 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 from the low-level CANN SDK 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 onsite) targets beginner to intermediate technical professionals seeking to comprehend how CANN and MindSpore components collaborate to manage the AI lifecycle and support infrastructure decisions.

Upon completion 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 toolchain in comparison to 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 covering the model flow from MindSpore to CANN.

Course Customization Options

• To request customized training for 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 maximize the performance of neural networks deployed on Ascend AI processors.

This instructor-led live training, available both online and onsite, targets advanced AI developers and system engineers looking to boost inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

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 effective 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 based on edge-case deployment scenarios.

Customization Options for the Course

• For customized training arrangements, please reach out to us.

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

This instructor-led live training, available online or onsite, targets intermediate-level AI professionals looking to construct, deploy, and refine vision and language models using the CANN SDK for real-world production scenarios.

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

• Deploy and optimize computer vision and NLP models utilizing CANN and AscendCL.
• Leverage CANN utilities to transform models and seamlessly integrate them into live processing pipelines.
• Enhance inference performance for applications such as detection, classification, and sentiment analysis.
• Construct real-time CV and NLP pipelines suitable for edge or cloud deployment environments.

Course Format

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

Course Customization Options

• For personalized training arrangements, please contact us directly.

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

This instructor-led, live training session (available online or on-site) targets advanced system developers looking to construct, deploy, and fine-tune custom operators for AI models using CANN’s TIK programming model alongside TVM compiler integration.

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

• Develop and test custom AI operators utilizing the TIK DSL for Ascend processors.
• Seamlessly integrate custom operations 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 combined with live demonstrations.
• Hands-on coding exercises for operators using TIK and TVM pipelines.
• Practical testing and tuning on Ascend hardware or simulators.

Course Customization Options

• To request a customized training version of this course, please reach out to us to arrange.

This instructor-led, live training in Argentina (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 completing this training, participants will be able to:

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

This instructor-led, live training in Argentina (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.