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Internship on  Edge AI Application Development with APSCHE and PHYTEC

Design and deploy Artificial Intelligence (AI) solutions on edge devices. This program integrates concepts of machine learning, AI model optimization, and deployment on resource-constrained embedded systems.

Duration: 16 Weeks
Course Structure:

  • 8 Weeks: Hands-on Training

  • 6 Weeks: Project Development

  • 2 Weeks: Interview Preparation and Personality Development

Detailed Course Content

Week 1-8: Hands-on Training Week 1: Introduction to Edge AI • Overview of Edge AI and its Applications • Edge AI vs Cloud AI: Benefits and Challenges • Hardware for Edge AI: GPUs, TPUs, and NPUs • Introduction to Edge AI Frameworks: TensorFlow Lite, ONNX, OpenVINO • Setting Up the Development Environment Week 2: AI Model Basics and Optimization • Fundamentals of AI/ML/DL Models • Understanding Model Compression (Quantization, Pruning) • Training and Converting Models for Edge Deployment • Tools for Model Optimization (TensorFlow Model Optimization Toolkit, PyTorch Mobile) Week 3: Edge AI Hardware Platforms • Introduction to Popular Edge AI Devices (NVIDIA Jetson, Google Coral, Raspberry Pi) • Setting Up Edge AI Devices • Hardware Accelerators and Their Use Cases • Benchmarking Models on Edge AI Platforms Week 4: Computer Vision on the Edge • Basics of Computer Vision Models (Classification, Object Detection, Segmentation) • Deploying Pre-Trained Models for Computer Vision on Edge Devices • Real-Time Image Processing Applications • Hands-on Practice: Building a Simple Object Detection Application Week 5: Audio and Natural Language Processing (NLP) on the Edge • Introduction to Audio and NLP Models for Edge Devices • Deploying Speech Recognition and Keyword Detection Models • Sentiment Analysis and Chatbot Development for Edge • Real-Time Applications: Wake Word Detection, Speech-to-Text Week 6: Edge AI Communication and Data Handling • Interfacing Edge AI with IoT Sensors • Real-Time Data Streaming and Processing • Edge AI and Cloud Communication via MQTT, HTTP, gRPC • Hands-on: Building a Simple IoT Application with Edge AI Week 7: Advanced Topics in Edge AI • Federated Learning for Edge Devices • AI Security and Privacy at the Edge • Energy-Efficient AI Model Deployment • Introduction to TinyML for Ultra-Low Power Devices Week 8: Debugging and Optimization • Profiling Tools for Edge AI (NVIDIA Nsight, TensorFlow Profiler) • Debugging Deployment Errors and Model Inference Issues • Improving Inference Speed and Reducing Latency • Review of All Concepts Covered in Hands-On Training

Week 9-14: Project Development Week 9: Project Ideation and Planning • Brainstorming Edge AI Project Ideas • Problem Definition and Solution Scoping • Creating a Detailed Development Plan Week 10: Data Collection and Model Preparation • Collecting and Preprocessing Data for the Project • Training and Testing AI Models for Specific Use Cases • Optimizing the Model for Edge Deployment Week 11: System Integration • Integrating AI Models with Edge AI Devices • Developing Middleware for Hardware Interaction • Interfacing with IoT Sensors or Actuators Week 12: Application Development • Building the Front-End and User Interface for the Application • Creating APIs for Real-Time Interaction • Ensuring Application Compatibility Across Platforms Week 13: Testing and Refinement • Debugging Hardware-Software Integration Issues • Testing Application Performance on Edge Devices • Refining Features and Optimizing Application Code Week 14: Final Demonstration • Final Integration and Deployment of the Project • Preparing a Presentation for Project Demonstration • Peer Review and Feedback

Week 15-16: Interview Preparation and Personality Development Week 15: Interview Preparation • Key Concepts and Questions in Edge AI Interviews • Hands-on Problem-Solving Exercises • Mock Interviews (Technical and Behavioral) • Building a Strong Portfolio with Projects Week 16: Personality Development • Communication and Presentation Skills • Resume Writing for AI Professionals • Leadership and Teamwork Skills for Collaborative Roles • Preparing for Workplace Challenges

Outcome By the end of this course, participants will: 1. Gain expertise in developing and deploying Edge AI applications. 2. Have completed a hands-on project to showcase in their portfolio. 3. Be confident and prepared for technical interviews and career opportunities. 4. Enhance communication, leadership, and interpersonal skills.

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