Rated Excellent

250+ Courses

30,000+ Learners

95+ Countries

INR ₹0.00
Cart

No products in the cart.

Sale!
DL2 1 scaled e1726894750840

Streaming Data Processing with AI Course

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.

SKU: 374 Categories: , , Tags: , , , , , , , , , , , , , , , , , , ,

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

Reviews

There are no reviews yet.

Be the first to review “Streaming Data Processing with AI Course”

Your email address will not be published. Required fields are marked *

Related products

Certification

  • Upon successful completion of the workshop, participants will be awarded a Certificate of Completion, validating their skills and knowledge in advanced AI ethics and regulatory frameworks. This certification can be added to your LinkedIn profile or shared with employers to demonstrate your commitment to ethical AI practices.

Achieve Excellence & Enter the Hall of Fame!

Elevate your research to the next level! Get your groundbreaking work considered for publication in  prestigious Open Access Journal (worth USD 1,000) and Opportunity to join esteemed Centre of Excellence. Network with industry leaders, access ongoing learning opportunities, and potentially earn a place in our coveted 

Hall of Fame.

Achieve excellence and solidify your reputation among the elite!

14 + years of experience

over 400000 customers

100% secure checkout

over 400000 customers

Well Researched Courses

verified sources