Introduction

Edge computing brings computation and data storage closer to the data source, reducing latency and bandwidth use while increasing the speed and efficiency of processing. With the growing volume of data generated by IoT devices, autonomous systems, and other sources, edge computing has become essential for real-time data analysis. This course focuses on applying data science techniques to edge computing environments, teaching participants how to process and analyze data at the edge for quick insights and decision-making.

Objectives

By the end of this course, participants will:

• Understand the principles of edge computing and how it differs from cloud computing.
• Learn how to deploy and manage data science models on edge devices.
• Gain hands-on experience with edge computing platforms like Raspberry Pi, NVIDIA Jetson, and Intel NUC.
• Understand how to preprocess and analyze real-time data from IoT devices and sensors at the edge.
• Explore key techniques for building and optimizing machine learning models for edge computing.
• Understand the challenges of working with limited resources and how to optimize algorithms for efficiency.

Who Should Attend?

This course is designed for:

• Data scientists and machine learning engineers interested in deploying models on edge devices.
• IoT developers and engineers looking to apply data science techniques to edge computing.
• Professionals in industries such as manufacturing, healthcare, and transportation where real-time data processing at the edge is crucial.
• Researchers and academics exploring the intersection of data science and edge computing.
• Anyone interested in learning how edge computing can enhance data analysis and decision-making.

Day 1: Introduction to Edge Computing

Morning Session: Fundamentals of Edge Computing

• What is edge computing and how it differs from cloud computing.
• Benefits of edge computing: Reduced latency, bandwidth optimization, and real-time decision-making.
• Edge computing architectures: Types of edge devices, fog computing, and cloud-edge integration.
• Examples of edge computing applications: IoT devices, smart cities, autonomous vehicles, and healthcare systems.
• Hands-on: Setting up an edge computing device (e.g., Raspberry Pi or NVIDIA Jetson).

Afternoon Session: Data at the Edge

• Data sources in edge computing: IoT devices, sensors, cameras, and autonomous systems.
• Real-time data streams and how edge devices process these streams.
• Edge data storage and management: Distributed databases, data lakes, and local storage solutions.
• Edge vs. cloud data processing: How and when to process data locally vs. in the cloud.
• Hands-on: Capturing data from IoT devices (e.g., temperature sensors, cameras) and streaming it to an edge device.

Day 2: Data Preprocessing and Feature Engineering at the Edge

Morning Session: Data Preprocessing Techniques

• Data quality challenges in edge computing: Noise, missing values, and unstructured data.
• Preprocessing techniques for real-time data: Filtering, normalization, and outlier detection.
• Techniques for reducing data before transmitting: Data aggregation, feature selection, and dimensionality reduction.
• Hands-on: Implementing real-time preprocessing on an edge device using Python (e.g., filtering sensor data).

Afternoon Session: Feature Engineering for Edge Computing

• Feature extraction from sensor and IoT data: Time series analysis, signal processing, and event detection.
• Creating meaningful features from raw data streams.
• Dimensionality reduction techniques for efficient processing at the edge (e.g., PCA, t-SNE).
• Hands-on: Creating features for a machine learning model based on real-time sensor data (e.g., temperature and humidity data).

Day 3: Machine Learning Models for Edge Computing

Morning Session: Introduction to Machine Learning on the Edge

• Overview of machine learning algorithms suited for edge computing: Decision trees, random forests, SVM, k-NN, and neural networks.
• The challenge of deploying machine learning models on edge devices: Limited compute power and memory.
• Model optimization techniques: Model compression, quantization, pruning, and knowledge distillation.
• Hands-on: Deploying a simple machine learning model on an edge device (e.g., classifying sensor data).

Afternoon Session: Model Training and Deployment

• Training models for edge computing: Considerations for local vs. cloud-based model training.
• Using cloud-based services to train models and then deploying them to edge devices.
• Techniques for model update and versioning at the edge.
• Hands-on: Training a model on the cloud and deploying it to an edge device for real-time inference.

Day 4: Advanced Topics in Edge Data Science

Morning Session: Real-Time Data Processing at the Edge

• Stream processing frameworks for the edge: Apache Kafka, Apache Flink, and edge analytics platforms.
• Processing data in real-time: Sliding windows, event-driven architectures, and batch processing.
• Latency considerations and optimizing real-time analytics for edge computing.
• Hands-on: Implementing real-time stream processing with an edge device (e.g., processing live data from sensors).

Afternoon Session: Edge Intelligence and Autonomous Systems

• Machine learning at the edge for autonomous systems: Self-driving cars, drones, and robotics.
• Edge computing in industrial applications: Predictive maintenance, anomaly detection, and quality control.
• Integration of edge devices with cloud services for hybrid models: Edge and cloud orchestration.
• Hands-on: Implementing a simple edge-based autonomous system (e.g., object detection with a camera).

Day 5: Edge Computing Deployment, Optimization, and Future Trends

Morning Session: Deploying and Managing Edge Computing Systems

• Deployment strategies for edge computing models: Continuous deployment, rollouts, and device management.
• Monitoring and maintenance of edge devices: Remote updates, diagnostics, and troubleshooting.
• Security considerations in edge computing: Data encryption, access control, and secure communication.
• Hands-on: Deploying a real-time monitoring system for edge devices.

Afternoon Session: Optimization Techniques and Future of Edge Computing

• Optimizing machine learning models for performance at the edge: Memory optimization, CPU/GPU considerations, and battery efficiency.
• Edge computing in the future: The role of 5G, AI, and quantum computing in enhancing edge capabilities.
• Case studies: Exploring successful implementations of edge data science in industries like healthcare, manufacturing, and smart cities.
• Final Project: Participants will work on a real-world edge data science problem, applying the techniques learned throughout the course.
• Wrap-up: Discussing the future of data science for edge computing and industry trends.

Materials and Tools:

• Software and Tools: Python, TensorFlow Lite, PyTorch, scikit-learn, Raspberry Pi, NVIDIA Jetson, Intel NUC, Apache Kafka, AWS IoT, Google Cloud IoT, Edge AI platforms.
• Hardware: Edge devices (Raspberry Pi, NVIDIA Jetson, etc.), IoT sensors, cameras.
• Resources: Course slides, Python notebooks, datasets for sensor data, tutorials on deploying models to edge devices.

Post-Course Support:

• Access to recorded sessions, course slides, and additional resources.
• Post-course webinars for exploring advanced edge computing and machine learning topics.
• Community forum for discussing projects, sharing ideas, and troubleshooting.
• One-on-one mentoring sessions for further guidance on edge data science challenges.