Streaming Data Processing with AI
Real-Time Intelligence: Harness AI for Streaming Data Processing
Early access to e-LMS included
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.
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.
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
Who Should Enrol?
Data engineers, AI researchers, software developers, and data scientists focusing on real-time data and AI integration.
Program Outcomes
- Expertise in building and deploying AI models for real-time data.
- Mastery in streaming data processing using tools like Kafka and Flink.
- Ability to scale and maintain AI models in live data environments.
- Proficiency in building fault-tolerant data pipelines for instant decision-making.
Fee Structure
Discounted: ₹8,499 | $112
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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