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Self Paced

Streaming Data Processing with AI

Real-Time Intelligence: Harness AI for Streaming Data Processing

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
3 Weeks

About

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
Final Project
- Build and deploy a real-time AI application using streaming data.
- Example: Real-Time Sentiment Analysis for a Live Twitter Feed or Real-Time Stock Price Prediction.

Participant’s Eligibility

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

Standard Fee: INR ~~4,998~~ USD 78

Discounted Fee: INR 2499 USD 39

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.

Program Deliverables

Access to e-LMS
Real Time Project for Dissertation
Project Guidance
Paper Publication Opportunity
Self Assessment
Final Examination
e-Certification
e-Marksheet

Future Career Prospects

Streaming Data Engineer
Real-Time AI Specialist
AI-Powered IoT Analyst
Real-Time Analytics Engineer
Data Streaming Architect
Predictive Analytics Engineer

Job Opportunities

Companies focused on real-time data processing in industries like finance, healthcare, IoT, and cybersecurity.
Startups building AI-driven streaming data solutions for dynamic environments.
Cloud providers offering real-time analytics and AI services.

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