Edge AI for Financial Services Training Course

This instructor-led, live training in China (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.

This instructor-led, live training in China (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.

This instructor-led, live training in China (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.

This instructor-led, live training in China (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

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

• Install and configure Tensorflow Lite on an embedded device.
• Understand the concepts and components underlying TensorFlow Lite.
• Convert existing models to TensorFlow Lite format for execution on embedded devices.
• Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
• Deploy a deep learning model on an embedded device running Linux.

This instructor-led, live training in China (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

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

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow, machine learning and deep learning.
• Load TensorFlow Models onto an Android device.
• Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

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

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow and machine learning on mobile devices.
• Load TensorFlow Models onto an iOS device.
• Run an iOS application capable of detecting and classifying an object captured through the device's camera.

This instructor-led, live training in China (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.