Streaming Data Processing with AI
Real-Time Intelligence: Harness AI for Streaming Data Processing
Skills you will gain:
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.
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.
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.
What you will learn?
- 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
Intended For :
Data engineers, AI researchers, software developers, and data scientists focusing on real-time data and AI integration.
Career Supporting Skills