Cambricon MLU Development with BANGPy and Neuware Training Course

This instructor-led, live training in [location] (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. 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.

The 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 for government.

This instructor-led, live training (online or onsite) is designed for 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:

• Understand how 6G will transform edge computing and IoT architectures for government applications.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations in public sector environments.
• Integrate AI and data analytics at the edge to support intelligent decision-making for government services.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures that meet government standards.
• Evaluate business and operational models enabled by 6G-edge convergence in a public sector context.

Format of the Course

• Interactive lectures and discussions tailored to government IT professionals.
• Case studies and applied architecture design exercises relevant to government projects.
• Hands-on simulation with optional edge or container tools, adapted for government use cases.

Course Customization Options

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

This instructor-led, live training (online or onsite) is designed for government and 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 the 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.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers, data scientists, and technology enthusiasts who seek to acquire practical skills in deploying artificial intelligence (AI) models on edge devices for a variety of 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 deployment on edge devices. - Implement practical AI solutions on edge devices. - Evaluate and enhance the performance of models deployed at the edge. - Address ethical and security considerations in Edge AI applications. This training is tailored to align with public sector workflows, governance, and accountability, ensuring that participants are well-equipped to apply these skills effectively for government projects.

This instructor-led, live training (available online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems. By the end of this training, participants will be able to: - Understand the security risks and vulnerabilities associated with Edge AI deployments. - Implement encryption and authentication methods to protect data. - Design resilient Edge AI architectures capable of withstanding cyber threats. - Apply secure deployment strategies for AI models in edge environments, ensuring alignment with public sector workflows and governance for government.

The Huawei Ascend CANN toolkit facilitates robust AI inference on edge devices such as the Ascend 310. CANN provides critical tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led, live training (online or onsite) is designed for intermediate-level AI developers and integrators who aim to deploy and optimize models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for deployment on the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines utilizing MindSpore Lite and AscendCL.
• Enhance model performance in compute- and memory-constrained environments.
• Deploy and monitor AI applications in practical edge scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work with edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customization Options for Government

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

Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide viable alternatives to CUDA, specifically designed for the local AI and HPC markets.

This instructor-led, live training (online or onsite) is targeted at advanced-level GPU programmers and infrastructure specialists who are looking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

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

• Evaluate the compatibility of current CUDA workloads with Chinese chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Compare performance metrics and identify optimization opportunities across different platforms.
• Address practical challenges in supporting and deploying applications across multiple architectures.

Format of the Course

• Interactive lectures and discussions.
• Hands-on code translation and performance comparison labs.
• Guided exercises focusing on multi-GPU adaptation strategies.

Course Customization Options for Government

• To request a customized training for this course based on your specific platform or CUDA project, please contact us to arrange.

This comprehensive course integrates the transformative power of artificial intelligence (AI) with the agility of edge computing. Participants will learn to deploy AI models directly on edge devices, covering topics from understanding convolutional neural network (CNN) architectures to mastering knowledge distillation and federated learning. This hands-on training is designed to equip professionals with the skills necessary to optimize AI performance for real-time processing and decision-making at the edge, ensuring alignment with public sector workflows and governance for government applications.

This instructor-led, live training in [location] (online or onsite) is aimed at beginner to intermediate agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming for government. 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 (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who aim to harness Edge AI for innovative solutions in the field of autonomous systems. By the end of this training, participants will be able to: - Comprehend the role and advantages of Edge AI in autonomous systems. - Develop and deploy AI models for real-time processing on edge devices. - Implement Edge AI solutions for applications in autonomous vehicles, drones, and robotics. - Design and optimize control systems using Edge AI methodologies. - Address ethical and regulatory considerations relevant to autonomous AI applications, ensuring compliance with standards for government use.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI, from conceptual foundations to practical implementation, including setup and deployment. By the end of this training, participants will be able to: - Understand the fundamental concepts of Edge AI. - Set up and configure Edge AI environments. - Develop, train, and optimize Edge AI models. - Deploy and manage Edge AI applications. - Integrate Edge AI with existing systems and workflows for government. - Address ethical considerations and best practices in Edge AI implementation.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish to implement and optimize computer vision models for real-time processing on edge devices. By the end of this training, participants will be able to: - Understand the fundamentals of Edge AI and its applications in computer vision. - Deploy optimized deep learning models on edge devices for real-time image and video analysis. - Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment. - Optimize AI models for performance, power efficiency, and low-latency inference. This training is designed to enhance the skills of professionals in the public sector, ensuring they are well-equipped to leverage advanced technologies for government applications.

This instructor-led, live training (online or onsite) is designed for intermediate-level finance professionals, fintech developers, and AI specialists who aim to implement Edge AI solutions in the financial sector. By the end of this training, participants will be able to: - Understand the role of Edge AI in financial services. - Implement fraud detection systems using Edge AI technologies. - Enhance customer service through AI-driven applications. - Apply Edge AI for risk management and informed decision-making. - Deploy and manage Edge AI solutions in financial environments, ensuring alignment with regulatory requirements for government and private sector entities.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and artificial intelligence developers who aim to utilize Edge AI for innovative healthcare solutions. By the end of this training, participants will be able to: - Understand the role and benefits of Edge AI in healthcare. - Develop and deploy AI models on edge devices for healthcare applications. - Implement Edge AI solutions in wearable devices and diagnostic tools. - Design and deploy patient monitoring systems using Edge AI. - Address ethical and regulatory considerations in healthcare AI applications, ensuring alignment with public sector workflows, governance, and accountability for government.

Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each providing specialized acceleration and profiling tools for production-scale AI workloads.

This instructor-led, live training (available online or onsite) is designed for advanced-level AI infrastructure and performance engineers who seek to optimize model inference and training workflows across multiple Chinese AI chip platforms.

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

• Evaluate models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and inefficiencies in memory and compute performance.
• Implement graph-level, kernel-level, and operator-level optimizations.
• Refine deployment pipelines to enhance throughput and reduce latency.

Format of the Course

• Interactive lecture and discussion sessions.
• Hands-on use of profiling and optimization tools for each platform.
• Guided exercises focusing on practical tuning scenarios.

Course Customization Options

• To request a customized training for government or other specific environments based on your performance needs or model type, please contact us to arrange.