Boston, MA – Regus at Federal Street

101 Federal St, Suite 1900, Boston, United States, 02110

The venue is located in the heart of Boston’s Financial District on Federal Street, easily accessible by car via I‑90, I‑93, and I‑95 with numerous nearby parking garages. From Logan International Airport (BOS), a taxi or rideshare takes approximately 20–25 minutes via I‑93 North. Public transit is seamless: the 15th Street and South Station – Financial District stops on MBTA's Red, Orange, and Silver lines are within a few minutes’ walk, and multiple bus routes traverse Federal and Congress Streets.

Burlington, MA – Regus at The District

1500 District Avenue, Burlington, United States, 01803

The venue is located within The District business campus at 1500 District Avenue, easily accessed by car via I‑89 or US‑3 with free on-site parking and additional municipal lots. From Boston’s Logan International Airport (BOS), approximately 15 miles southeast, a taxi or rideshare takes 25–30 minutes via I‑93 North and I‑95 North. Public transit is served by MBTA commuter rail to South Burlington, with the Campus Parkway #2 bus running directly past the entrance—placing the venue within a short walk for attendees without cars.

Cambridge, MA – Regus at Harvard Square

One Mifflin Place, Suite 400, Cambridge, United States, 02138

The venue is ideally situated in the heart of Harvard Square at One Mifflin Place, easily accessed by car via Route 2, MA‑2A, or Memorial Drive, with pay-to-park garages and metered street spaces nearby. From Boston Logan International Airport (BOS), taxis or rideshares take approximately 25–30 minutes via I‑90 West and Cambridge Street. Alternatively, public transit offers seamless connections: take the MBTA Red Line to Harvard station, and the entrance is a 3-minute walk from the square, providing effortless access for attendees without cars.

Worcester, MA – Regus at Campus Square

370 Main Street, Worcester, United States, 01608

The venue is located in the LEED Gold–certified Campus Square building at 370 Main Street in the heart of Worcester, easily reachable by car via I‑290 or I‑90, with abundant on-site parking and municipal garages nearby. From Worcester Regional Airport (ORH), about 5 miles west, a taxi or rideshare takes 10–15 minutes via Airport Drive and Main Street. Public transit is seamless: multiple WRTA bus routes stop just outside the building, and Worcester Amtrak/MBTA train station is an easy 12-minute walk, allowing easy access without a car.

Explore Our Courses

Federated Learning and Edge AI

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at advanced-level AI researchers, data scientists, and security specialists who wish to implement federated learning techniques for training AI models across multiple edge devices while preserving data privacy.

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

  • Understand the principles and benefits of federated learning in Edge AI.
  • Implement federated learning models using TensorFlow Federated and PyTorch.
  • Optimize AI training across distributed edge devices.
  • Address data privacy and security challenges in federated learning.
  • Deploy and monitor federated learning systems in real-world applications.
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Edge AI for Agriculture: Smart Farming and Precision Monitoring

 21 Hours

This instructor-led, live training in Massachusetts (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.
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Building Secure and Resilient Edge AI Systems

 21 Hours

This instructor-led, live training in Massachusetts (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.
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Edge AI for Retail: Enhancing Customer Experience and Operations

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at beginner-level to intermediate-level retail technologists, AI developers, and business analysts who wish to apply Edge AI solutions for smart checkout systems, inventory management, and personalized customer engagement.

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

  • Understand how Edge AI enhances retail operations and customer experience.
  • Implement AI-powered smart checkout and cashier-less payment systems.
  • Optimize inventory management with real-time tracking and analytics.
  • Utilize computer vision and AI for personalized in-store experiences.
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5G and Edge AI: Enabling Ultra-Low Latency Applications

 21 Hours

This instructor-led, live training in Massachusetts (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.
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Fine-Tuning Lightweight Models for Edge AI Deployment

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

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

  • Select and adapt pre-trained models suitable for edge deployment.
  • Apply quantization, pruning, and other compression techniques to reduce model size and latency.
  • Fine-tune models using transfer learning for task-specific performance.
  • Deploy optimized models on real edge hardware platforms.
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Edge AI for Computer Vision: Real-Time Image Processing

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level to advanced-level 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.
  • Use frameworks like TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
  • Optimize AI models for performance, power efficiency, and low-latency inference.
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TinyML: Running AI on Ultra-Low-Power Edge Devices

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

  • Understand the fundamentals of TinyML and edge AI.
  • Deploy lightweight AI models on microcontrollers.
  • Optimize AI inference for low-power consumption.
  • Integrate TinyML with real-world IoT applications.
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Edge AI and Robotics: Enabling Autonomous Systems

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers, AI developers, and automation specialists who wish to implement Edge AI for robotics applications.

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

  • Understand the role of Edge AI in autonomous systems.
  • Deploy AI models on edge devices for real-time robotics.
  • Optimize AI performance for low-latency decision-making.
  • Integrate computer vision and sensor fusion for robotic autonomy.
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Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at advanced-level AI engineers, embedded developers, and hardware engineers who wish to implement AI models on low-power devices while minimizing energy consumption.

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

  • Understand the challenges of running AI on energy-efficient devices.
  • Optimize neural networks for low-power inference.
  • Utilize quantization, pruning, and model compression techniques.
  • Deploy AI models on edge hardware with minimal power usage.
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Deploying AI Models on Edge Devices with NVIDIA Jetson

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level AI developers, embedded engineers, and robotics engineers who wish to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications.

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

  • Understand the fundamentals of edge AI and NVIDIA Jetson hardware.
  • Optimize AI models for deployment on edge devices.
  • Use TensorRT for accelerating deep learning inference.
  • Deploy AI models using JetPack SDK and ONNX Runtime.
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Optimizing AI Models for Edge Devices

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

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

  • Understand the challenges and requirements of deploying AI models on edge devices.
  • Apply model compression techniques to reduce the size and complexity of AI models.
  • Utilize quantization methods to enhance model efficiency on edge hardware.
  • Implement pruning and other optimization techniques to improve model performance.
  • Deploy optimized AI models on various edge devices.
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Building AI Solutions on the Edge

 14 Hours

This instructor-led, live training in Massachusetts (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.
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Edge AI for Financial Services

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.

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.
  • Enhance customer service through AI-driven solutions.
  • Apply Edge AI for risk management and decision-making.
  • Deploy and manage Edge AI solutions in financial environments.
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Edge AI in Industrial Automation

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.

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

  • Understand the role of Edge AI in industrial automation.
  • Implement predictive maintenance solutions using Edge AI.
  • Apply AI techniques for quality control in manufacturing processes.
  • Optimize industrial processes using Edge AI.
  • Deploy and manage Edge AI solutions in industrial environments.
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Edge AI with TensorFlow Lite

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

  • Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
  • Develop and optimize AI models using TensorFlow Lite.
  • Deploy TensorFlow Lite models on various edge devices.
  • Utilize tools and techniques for model conversion and optimization.
  • Implement practical Edge AI applications using TensorFlow Lite.
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Edge AI for Smart Cities

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level urban planners, civil engineers, and smart city project managers who wish to leverage Edge AI for smart city initiatives.

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

  • Understand the role of Edge AI in smart city infrastructures.
  • Implement Edge AI solutions for traffic management and surveillance.
  • Optimize urban resources using Edge AI technologies.
  • Integrate Edge AI with existing smart city systems.
  • Address ethical and regulatory considerations in smart city deployments.
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Security and Privacy in Edge AI

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who wish to secure and ethically deploy Edge AI solutions.

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

  • Understand the security and privacy challenges in Edge AI.
  • Implement best practices for securing edge devices and data.
  • Develop strategies to mitigate security risks in Edge AI deployments.
  • Address ethical considerations and ensure compliance with regulations.
  • Conduct security assessments and audits for Edge AI applications.
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Edge AI in Autonomous Systems

 14 Hours

This instructor-led, live training in Massachusetts (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.
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Edge AI for Healthcare

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage 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.
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Advanced Edge AI Techniques

 14 Hours

This instructor-led, live training in Massachusetts (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.
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Edge AI for IoT Applications

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

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

  • Understand the fundamentals of Edge AI and its application in IoT.
  • Set up and configure Edge AI environments for IoT devices.
  • Develop and deploy AI models on edge devices for IoT applications.
  • Implement real-time data processing and decision-making in IoT systems.
  • Integrate Edge AI with various IoT protocols and platforms.
  • Address ethical considerations and best practices in Edge AI for IoT.
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TinyML for IoT Applications

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

  • Understand the fundamentals of TinyML and its applications in IoT.
  • Set up a TinyML development environment for IoT projects.
  • Develop and deploy ML models on low-power microcontrollers.
  • Implement predictive maintenance and anomaly detection using TinyML.
  • Optimize TinyML models for efficient power and memory usage.
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Edge AI: From Concept to Implementation

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI from concept 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.
  • Address ethical considerations and best practices in Edge AI implementation.
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Deploying AI on Microcontrollers with TinyML

 21 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

  • Understand the fundamentals of TinyML and its benefits for edge AI applications.
  • Set up a development environment for TinyML projects.
  • Train, optimize, and deploy AI models on low-power microcontrollers.
  • Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
  • Optimize AI models for power efficiency and memory constraints.
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Cambricon MLU Development with BANGPy and Neuware

 21 Hours

Cambricon MLUs (Machine Learning Units) are specialized AI chips optimized for inference and training in edge and datacenter scenarios.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish 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 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 Neuware runtime.
  • Integrate ML workflows with MLU-specific acceleration features.

Format of the Course

  • Interactive lecture and discussion.
  • Hands-on use of BANGPy and Neuware for development and deployment.
  • Guided exercises focused on optimization, integration, and testing.

Course Customization Options

  • To request a customized training for this course based on your Cambricon device model or use case, please contact us to arrange.
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Introduction to Edge AI

 14 Hours

This instructor-led, live training in Massachusetts (online or onsite) is aimed at beginner-level developers and IT professionals who wish to understand the fundamentals of Edge AI and its introductory applications.

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

  • Understand the basic concepts and architecture of Edge AI.
  • Set up and configure Edge AI environments.
  • Develop and deploy simple Edge AI applications.
  • Identify and understand the use cases and benefits of Edge AI.
Read more...

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Upcoming Courses

Building AI Solutions on the Edge

2025-09-01 9:30 am
14 hours
Burlington, MA – Regus at The District
$ 2703 (Online)
$ 3703 (Classroom)

Edge AI for Computer Vision: Real-Time Image Processing

2025-09-15 9:30 am
21 hours
Burlington, MA – Regus at The District
$ 4054 (Online)
$ 5554 (Classroom)

Edge AI in Industrial Automation

2025-09-29 9:30 am
14 hours
Burlington, MA – Regus at The District
$ 2703 (Online)
$ 3703 (Classroom)

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