Streaming Data Processing with AI

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Mentor Based

Program Syllabus

Module 1

About This Course

Participants will learn how to leverage AI for processing continuous data streams. The program covers stream processing platforms like Apache Kafka and Flink, machine learning models for real-time data, and predictive analytics for instant insights. Emphasis is placed on handling dynamic data at scale using AI.

Module 2

Aim

To provide in-depth knowledge on how to process real-time data streams using AI technologies. This course focuses on building scalable systems that can ingest, process, and analyze streaming data, enabling instant decision-making in applications like finance, IoT, and e-commerce.

Module 3

Program Objectives

Understand the fundamentals of streaming data and its applications in AI.
Learn to build scalable streaming pipelines using Kafka, Flink, or Spark Streaming.
Apply AI models for real-time analytics and anomaly detection.
Master techniques to ensure scalability and fault tolerance in streaming data systems.
Gain hands-on experience with real-time AI solutions.

Module 4

Program Structure

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)
Fundamentals of Data Stream Processing
- Data Streams and Real-Time Processing Requirements
- Overview of Stream Processing Architectures
- Data Ingestion in Real Time (Kafka, Flume)
Streaming Data Processing Tools and Frameworks
- Apache Spark Streaming
- Apache Flink for Real-Time Analytics
- Other Tools: Storm, Heron, and Samza
Data Preprocessing in Streaming Systems
- Real-Time Data Cleaning and Transformation
- Sliding Windows and Time-Based Processing
- Aggregation Techniques for Streaming Data
Machine Learning on Streaming Data
- Incremental Learning Algorithms
- Online Learning vs. Offline Learning
- Stream Processing in AI: Spark MLlib, MOA (Massive Online Analysis)
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)
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)
Streaming Data for Predictive Analytics
- Real-Time Prediction Pipelines
- Anomaly Detection in Streaming Data
- Fraud Detection in Financial Data Streams
Real-Time Data Visualization and Dashboards
- Building Dashboards for Streaming Data (Grafana, Kibana)
- Visualizing Real-Time AI Predictions
- Monitoring and Alerting in Streaming Systems
Scaling and Optimizing Streaming Systems
- Handling High-Throughput and Low-Latency Requirements
- Distributed Streaming Systems (Kubernetes, Docker)
- Optimizing AI Models in Real-Time Systems
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

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.