Streaming Data Processing with AI

Aim

Program Objectives

Program Structure

1. Introduction to Streaming Data and AI
   • What is Streaming Data?
   • Batch vs. Stream Processing
   • Applications of Streaming Data in AI (e.g., Financial Systems, Real-time Recommendations)
2. Fundamentals of Data Stream Processing
   • Data Streams and Real-Time Processing Requirements
   • Overview of Stream Processing Architectures
   • Data Ingestion in Real Time (Kafka, Flume)
3. Streaming Data Processing Tools and Frameworks
   • Apache Spark Streaming
   • Apache Flink for Real-Time Analytics
   • Other Tools: Storm, Heron, and Samza
4. Data Preprocessing in Streaming Systems
   • Real-Time Data Cleaning and Transformation
   • Sliding Windows and Time-Based Processing
   • Aggregation Techniques for Streaming Data
5. Machine Learning on Streaming Data
   • Incremental Learning Algorithms
   • Online Learning vs. Offline Learning
   • Stream Processing in AI: Spark MLlib, MOA (Massive Online Analysis)
6. Deep Learning on Streaming Data
   • Real-Time Neural Network Architectures for Stream Data
   • Deploying Deep Learning Models on Streaming Frameworks
   • Use Cases (e.g., Real-Time Image Classification, Video Analytics)
7. Natural Language Processing (NLP) on Streaming Data
   • Processing Live Text Streams (e.g., Social Media, News)
   • Real-Time Sentiment Analysis and Topic Detection
   • Stream Processing for NLP Tasks (BERT, GPT on Streaming Data)
8. Streaming Data for Predictive Analytics
   • Real-Time Prediction Pipelines
   • Anomaly Detection in Streaming Data
   • Fraud Detection in Financial Data Streams
9. Real-Time Data Visualization and Dashboards
   • Building Dashboards for Streaming Data (Grafana, Kibana)
   • Visualizing Real-Time AI Predictions
   • Monitoring and Alerting in Streaming Systems
10. Scaling and Optimizing Streaming Systems
    • Handling High-Throughput and Low-Latency Requirements
    • Distributed Streaming Systems (Kubernetes, Docker)
    • Optimizing AI Models in Real-Time Systems
11. Security and Privacy in Streaming Data
    • Ensuring Data Security in Streaming Systems
    • Data Privacy Challenges in Real-Time Processing
    • Handling Sensitive Data in Streaming AI Applications

Who Should Enrol?

Program Outcomes

Fee Structure

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