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Streaming Data Processing with AI Course – 3 Weeks

Original price was: INR ₹4,998.00.Current price is: INR ₹2,499.00.

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.

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Aim

This program aims to teach professionals and researchers how to effectively process real-time data streams using AI technologies. It covers the development of scalable systems for ingesting, processing, and analyzing streaming data, enabling instant decision-making in industries like finance, IoT, and e-commerce.

Program Objectives

  • Learn Streaming Data Fundamentals: Understand the core concepts and applications of streaming data in AI.
  • Build Scalable Pipelines: Gain skills in building scalable data pipelines using Kafka, Flink, or Spark Streaming.
  • AI for Real-Time Analytics: Apply AI models for real-time predictions and anomaly detection.
  • Ensure Scalability and Fault Tolerance: Master techniques to create fault-tolerant streaming systems that scale efficiently.
  • Hands-On AI Solutions: Develop practical experience with real-time AI solutions.

Program Structure

Module 1: Introduction to Streaming Data and AI

  • What is Streaming Data?
  • Batch vs. Stream Processing: Understand the differences and use cases.
  • Applications of Streaming Data in AI: Financial systems, real-time recommendations, IoT applications.

Module 2: Fundamentals of Data Stream Processing

  • Real-Time Processing Requirements: Speed, accuracy, and consistency.
  • Overview of Stream Processing Architectures.
  • Data Ingestion Tools: Using Kafka, Flume, and other tools for real-time data intake.

Module 3: Streaming Data Processing Tools and Frameworks

  • Apache Spark Streaming: Techniques for real-time data analysis.
  • Apache Flink for real-time analytics and stream processing.
  • Other tools: Storm, Heron, and Samza for distributed real-time computations.

Module 4: Data Preprocessing in Streaming Systems

  • Real-Time Data Cleaning and transformation techniques.
  • Sliding Windows and Time-Based Processing: Real-time analytics techniques.
  • Aggregating data in real-time streams for AI-driven insights.

Module 5: Machine Learning on Streaming Data

  • Incremental Learning Algorithms: Online learning vs. traditional batch learning.
  • Stream processing with AI Tools: Using Spark MLlib, MOA for real-time learning.
  • Deep Learning for streaming data: Real-time neural networks for classification and prediction.

Module 6: Deep Learning on Streaming Data

  • Deploying Neural Networks on streaming platforms.
  • Real-world applications like real-time image classification and video analytics.
  • Optimizing CNNs for stream data processing.

Module 7: Natural Language Processing (NLP) on Streaming Data

  • Live Text Stream Processing: Analyze real-time social media or news streams.
  • Real-time Sentiment Analysis and Topic Detection.
  • Applying BERT and GPT models on live data streams.

Module 8: Streaming Data for Predictive Analytics

  • Building real-time prediction pipelines.
  • Anomaly Detection and Fraud Detection in financial data streams.
  • Case studies: Real-time fraud detection in banking and financial systems.

Module 9: Real-Time Data Visualization and Dashboards

  • Building Dashboards for streaming data visualization using Grafana and Kibana.
  • Real-time monitoring and alerting based on AI predictions.
  • Practical use of data visualization for streaming analytics.

Module 10: Scaling and Optimizing Streaming Systems

  • High-Throughput and Low-Latency requirements: How to manage performance.
  • Distributed Streaming Systems: Implementing real-time systems using Kubernetes, Docker.
  • Techniques to optimize AI models in live environments.

Module 11: Security and Privacy in Streaming Data

  • Ensuring data security in real-time processing systems.
  • Managing privacy challenges in real-time streaming AI.
  • Handling sensitive data securely in live AI applications.

Final Project

  • Build and Deploy a real-time AI application using streaming data.
  • Example projects: Real-time sentiment analysis for a live Twitter feed, or real-time stock price prediction using AI.

Participant Eligibility

  • Data Engineers: Interested in building streaming pipelines and AI integration.
  • AI Researchers: Focused on real-time decision-making using AI.
  • Software Developers: Working on real-time AI-driven applications.
  • Data Scientists: Specializing in dynamic and live data analysis.

Program Outcomes

  • Real-Time AI Deployment Skills: Master the deployment of AI models for real-time data streams.
  • Expertise in Streaming Data: Learn how to process streaming data using tools like Kafka, Flink, and Spark.
  • Scalability and Fault Tolerance: Ability to build scalable, fault-tolerant data pipelines.
  • Instant Decision-Making: Proficiency in deploying AI solutions for real-time decision-making in industries like finance and IoT.

Program Deliverables

  • Access to e-LMS: Full access to course materials online.
  • Real-Time Projects: Develop real-time AI solutions for live data analysis.
  • Project Guidance: Mentorship to guide you through building AI-powered streaming systems.
  • Research Paper Opportunity: Support for publishing your work on real-time AI integration.
  • Final Examination: Certification awarded based on the completion of assignments, projects, and exams.
  • e-Certification: Receive an e-certificate upon successful completion of the program.

Future Career Prospects

  • Streaming Data Engineer: Design and maintain streaming data infrastructure.
  • Real-Time AI Specialist: Focus on deploying AI for live data analysis and decision-making.
  • AI-Powered IoT Analyst: Apply AI techniques to IoT data streams for predictive maintenance and smart systems.
  • Real-Time Analytics Engineer: Build AI models for high-speed, real-time data processing.
  • Data Streaming Architect: Lead the design of distributed streaming systems for enterprises.
  • Predictive Analytics Engineer: Use real-time data for predictive modeling and anomaly detection.

Job Opportunities

  • Companies focusing on real-time data processing in industries like finance, healthcare, IoT, and cybersecurity.
  • Startups building AI-powered streaming data solutions for fast-paced, dynamic environments.
  • Cloud Providers offering real-time analytics and AI services for diverse industries.
MODE

Online/ e-LMS
TYPE

Self Paced
LEVEL

Moderate
DURATION

3 Weeks

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