CANN for Edge AI Deployment Training Course

This instructor-led, live training in US (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 for government entities.

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.

This forward-looking curriculum examines the convergence of sixth-generation (6G) wireless systems with edge computing, Internet of Things (IoT) networks, and artificial intelligence (AI) data processing. The program supports the development of adaptive, intelligent infrastructures characterized by minimal latency and high responsiveness.

Delivered via live instructor-led sessions either online or onsite, this training targets intermediate-level IT architects seeking to design next-generation distributed environments. Participants will explore the strategic integration of 6G connectivity and intelligent edge systems to enhance operational capabilities for government and public sector needs.

Upon completing this program, participants will be able to:

• Analyze the transformative impact of 6G on edge computing and IoT architectures.
• Engineer distributed systems optimized for ultra-low latency, high bandwidth, and autonomous functions.
• Deploy AI and data analytics solutions at the network edge to facilitate intelligent decision-making processes.
• Develop scalable, secure, and resilient infrastructures prepared for 6G deployment.
• Assess business and operational frameworks supported by the convergence of 6G and edge technologies.

Course Delivery Format

• Interactive lectures and structured discussions.
• Case study analysis and applied architecture design exercises.
• Practical simulations utilizing edge or container-based tools, as applicable.

Course Customization Options

• To request a customized training solution for government agencies and departments, please contact us to arrange.

This facilitated, live instruction delivered via US (remote or on-premises) targets experienced artificial intelligence practitioners, researchers, and software engineers who seek to acquire expertise in the most recent developments within Edge AI. The program is designed for government professionals aiming to optimize their models for edge infrastructure and investigate specialized implementations across diverse operational sectors.

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

• Examine sophisticated methodologies for developing and refining Edge AI models.
• Execute advanced strategies for deploying AI systems on edge hardware.
• Leverage specialized tools and frameworks tailored for high-level Edge AI applications.
• Enhance the performance and operational efficiency of Edge AI solutions.
• Investigate novel use cases and emerging trends in Edge AI relevant for government initiatives.
• Navigate complex ethical and security requirements associated with Edge AI deployments.

Huawei Ascend comprises a series of artificial intelligence processors engineered to deliver high-performance capabilities for both inference and training workloads.

This instructor-led, live training session, available via online or onsite formats, targets intermediate-level AI engineers and data scientists seeking to build and optimize neural network models utilizing the Huawei Ascend platform alongside the CANN toolkit.

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

• Establish and configure the CANN development environment.
• Create AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the implementation of custom operators and tiling techniques.
• Deploy models within edge or cloud infrastructure.

Course Delivery Format

• Engaging lectures and discussions.
• Practical application of Huawei Ascend hardware and the CANN toolkit within sample applications.
• Supervised exercises centered on model construction, training, and deployment processes.

Course Customization Options

• Agencies seeking tailored training aligned with their specific infrastructure or datasets may contact the provider to arrange these services for government use cases.

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

This instructor-led session, available via online or onsite delivery, is designed for intermediate-level developers and engineers seeking to efficiently implement trained AI models on Huawei Ascend hardware utilizing the CANN toolkit alongside frameworks such as MindSpore, TensorFlow, or PyTorch. The curriculum emphasizes scalable solutions tailored for government and enterprise environments for government operations.

Upon completion of this training, participants will demonstrate proficiency in:

• Analyzing the CANN architecture and its function within the AI deployment lifecycle.
• Converting and adapting models from widely used frameworks into formats compatible with Ascend processors.
• Utilizing utilities such as ATC, OM model conversion, and MindSpore to support inference tasks across edge and cloud infrastructure.
• Identifying deployment challenges and executing performance optimizations on Ascend hardware platforms.

Course Delivery Structure

• Interactive instruction combined with technical demonstrations.
• Practical laboratory exercises utilizing CANN tools within Ascend simulator or hardware environments.
• Application of deployment strategies based on real-world AI use cases.

Training Adaptation Options

• For agencies requiring a tailored curriculum, please contact our administrative team to coordinate custom arrangements.

This instructor-led, live training offered in US (via online or onsite formats) is designed for intermediate-level developers, data scientists, and technology professionals seeking practical competencies in deploying artificial intelligence models on edge devices for diverse operational use cases.

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

• Comprehend the foundational principles of Edge AI and its associated advantages.
• Establish and configure environments for edge computing infrastructure.
• Create, train, and optimize artificial intelligence models for edge deployment.
• Execute practical AI solutions on edge hardware platforms.
• Assess and enhance the performance metrics of models deployed at the edge.
• Manage ethical implications and security protocols within Edge AI applications.

This instructor-led, live training, available via US (online or onsite), is designed for advanced cybersecurity professionals, artificial intelligence engineers, and IoT developers seeking to establish comprehensive security protocols and resilience frameworks for Edge AI systems.

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

• Identify and assess security risks and vulnerabilities associated with Edge AI deployments within government contexts.
• Deploy encryption and authentication mechanisms to safeguard sensitive data.
• Architect resilient Edge AI environments capable of mitigating cyber threats.
• Execute secure strategies for the deployment of AI models at the edge.

Cambricon Machine Learning Units (MLUs) represent specialized artificial intelligence processors engineered to support inference and training workloads across both edge computing and data center environments.

This instructor-led live training, available either online or onsite, targets intermediate-level software developers seeking to construct and deploy artificial intelligence models utilizing the BANGPy framework and Neuware SDK within Cambricon MLU hardware architectures. The program is designed for government applications where efficient AI deployment is critical.

Upon completion of this instructional session, participants will be capable of:

• Establishing and configuring development environments for both BANGPy and Neuware.
• Creating and refining Python- and C++-based models optimized for Cambricon MLU performance.
• Deploying trained models to edge and data center systems operating on the Neuware runtime.
• Integrating machine learning workflows with acceleration features specific to MLU technology.

Course Format

• Interactive lectures paired with technical discussions.
• Practical application of BANGPy and Neuware for development and deployment tasks.
• Structured exercises focusing on optimization, system integration, and validation.

Customization Options

• Agencies may request tailored training sessions aligned with their specific Cambricon device models or operational use cases by contacting the training provider to arrange details.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors, offering critical resources for government workloads that require scalable intelligence solutions.

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customization Options

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

Huawei provides a cohesive AI infrastructure that spans from the foundational CANN software development kit to the advanced MindSpore framework. This environment is specifically engineered to facilitate efficient AI model development and deployment on Ascend-based computing hardware.

This guided instructional session, available via live online or in-person formats, targets technical practitioners at beginner and intermediate proficiency levels. The curriculum focuses on elucidating the synergistic relationship between CANN and MindSpore components to inform strategic AI lifecycle management and infrastructure planning. Designed for government professionals, the training supports informed decision-making regarding public sector technology adoption.

Upon completion of this instructional module, participants will be equipped to:

• Analyze the hierarchical structure of Huawei’s AI computing architecture.
• Assess the role of CANN in optimizing model performance and executing hardware-level deployment.
• Evaluate the MindSpore framework and associated toolchain against comparable industry solutions.
• Determine the appropriate placement of Huawei’s AI stack within enterprise, cloud-based, or on-premises operational environments.

Course Delivery Format

• Structured lectures coupled with interactive group discussions.
• Real-time system demonstrations and analysis of practical case studies.
• Optional supervised laboratory exercises focusing on the workflow from MindSpore to CANN.

Customization Availability

• Organizations seeking tailored training for this course are encouraged to contact our team to arrange specific requirements.

The Compute Architecture for Neural Networks (CANN) SDK serves as Huawei’s foundational platform for AI computing. It enables engineers to refine and enhance the operational efficiency of neural networks deployed on Ascend AI processors.

This instructor-led training program, available through online or onsite delivery methods, is designed for advanced artificial intelligence developers and system engineers seeking to maximize inference capabilities. Participants will utilize CANN’s comprehensive toolset, specifically focusing on the Graph Engine, TIK, and custom operator development frameworks.

Upon completion of this educational module, participants will demonstrate proficiency in:

• Analyzing CANN's runtime architecture and performance lifecycle.
• Leveraging profiling tools and the Graph Engine for detailed performance assessment and optimization.
• Developing and refining custom operators using TIK and TVM technologies.
• Addressing memory constraints and enhancing model throughput efficiency.

Course Delivery Format

• Structured interactive lectures and technical discussions.
• Practical laboratory exercises featuring real-time profiling and operator tuning.
• Targeted optimization workshops utilizing edge-case deployment scenarios.

Customization Options for Government

• To request a customized training curriculum tailored to specific organizational needs, please contact our support team to make arrangements.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time artificial intelligence workloads in computer vision and natural language processing, particularly when utilized with Huawei Ascend hardware.

This instructor-led training program, available via online or onsite delivery, is designed for intermediate-level AI professionals seeking to develop, deploy, and optimize vision and language models using the CANN SDK for government and production-grade applications.

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

• Deploy and optimize computer vision (CV) and natural language processing (NLP) models utilizing CANN and AscendCL.
• Leverage CANN utilities to convert models and integrate them into operational pipelines.
• Enhance inference performance for critical tasks including detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment architectures.

Course Format

• Interactive lectures coupled with live demonstrations.
• Practical laboratory exercises focused on model deployment and performance profiling.
• Live pipeline development using authentic CV and NLP use cases.

Course Customization Options

• Agencies requiring customized training for government-specific needs should contact us to arrange tailored sessions.

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

This instructor-led, live training (available online or onsite) targets advanced system developers seeking to build, deploy, and tune custom operators for AI models via CANN’s TIK programming model and TVM compiler integration. Designed specifically for government applications, this course supports critical computational needs.

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

• Writing and validating custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph infrastructure.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking procedures.
• Debugging and optimizing instruction-level performance for specialized computation patterns.

Course Format

• Interactive lectures combined with technical demonstrations.
• Practical coding exercises involving operators through TIK and TVM pipelines.
• Testing and tuning activities performed on Ascend hardware or simulation environments.

Customization Options

• To request tailored training for this curriculum, please contact the program coordinators to make arrangements.

This comprehensive program integrates advanced artificial intelligence with the responsiveness of edge computing. Participants will acquire the capabilities required to deploy machine learning models directly on localized hardware, encompassing an understanding of convolutional neural network structures as well as proficiency in knowledge distillation and federated learning methodologies. The curriculum provides practical instruction designed to enhance AI operational efficiency for real-time data processing and immediate decision-making at the network periphery, specifically tailored for government applications and infrastructure.

This instructor-led, live training in US (online or onsite) is designed for beginner to intermediate-level professionals in agritech, IoT, and artificial intelligence who aim to develop and deploy Edge AI solutions for smart farming applications. The program provides resources specifically for government entities seeking to modernize agricultural practices.

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

• Understanding the role of Edge AI in precision agriculture.
• Implementing AI-driven crop and livestock monitoring systems.
• Developing automated irrigation and environmental sensing solutions.
• Optimizing agricultural efficiency using real-time Edge AI analytics.