Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Streaming Data Processing with AI is about intelligence in motion: making decisions on data as it arrives rather than hours later in a batch. You learn the architecture of streaming systems — message queues, stream processors and windowing — using tools such as Kafka and Spark Structured Streaming. The course then layers machine learning on top: running models on live streams for real-time detection, alerting and recommendation, and handling the hard parts of stateful, always-on inference. You leave able to design and reason about a low-latency, AI-driven streaming pipeline. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers real-time streaming data processing with AI — stream architectures, windowing and online inference for low-latency decisions on continuous data.
📋 Course Objectives
1. Explain streaming architectures and message queues.
2. Process streams with windowing and stateful operators.
3. Use tools such as Kafka and Spark Structured Streaming.
4. Run models on live data for real-time inference.
5. Design low-latency, always-on AI pipelines.
👥 Who Should Enroll?
• Data engineers building real-time systems
• Backend engineers moving into streaming
• Data scientists deploying online inference
• Students specialising in real-time data
🚀 Key Learning Outcomes
• The ability to build a real-time streaming pipeline.
• Experience running models on live data.
• A streaming-analytics project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Streaming Data Processing Foundations
Develop a comprehensive understanding of AI and machine learning concepts, including supervised and unsupervised learning techniques • Analyze mathematical foundations of streaming data processing, including probability, statistics, and linear algebra • Design a basic streaming data processing pipeline using AI and machine learning algorithms
Data Engineering, Preprocessing, and Feature Pipelines
Configure data ingestion and processing workflows using Apache Kafka, Apache Beam, or similar technologies • Implement data preprocessing techniques, including data cleaning, feature scaling, and data transformation • Evaluate the effectiveness of different feature engineering techniques, including feature selection and dimensionality reduction
Model Architecture, Algorithm Design, and Streaming Data Processing Methods
Design and implement deep learning models for streaming data processing, including convolutional neural networks and recurrent neural networks • Develop and evaluate the performance of different algorithmic techniques, including online learning and incremental learning • Optimize model architecture and hyperparameters for improved performance and efficiency
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate machine learning models using various metrics, including accuracy, precision, recall, and F1 score • Implement hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization • Analyze and visualize the results of model training and evaluation using tools like TensorBoard or Matplotlib
Deployment, MLOps, and Production Workflows
Deploy trained models to production environments using containerization techniques, such as Docker • Implement monitoring and logging mechanisms to track model performance and data quality • Develop and maintain MLOps workflows, including model serving, monitoring, and updating
Ethics, Bias Mitigation, and Responsible AI Practices
Evaluate the ethical implications of AI and machine learning models, including bias, fairness, and transparency • Implement techniques for bias mitigation and fairness, including data preprocessing and model regularization • Develop and implement responsible AI practices, including model interpretability and explainability
Industry Integration, Business Applications, and Case Studies
Analyze real-world case studies of streaming data processing with AI in various industries, including finance, healthcare, and retail • Develop and evaluate the business value of AI and machine learning models, including return on investment and cost-benefit analysis • Implement AI and machine learning models in industry-specific applications, including recommender systems and predictive maintenance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Apache Kafka |
| Covered Tool / Platform | Apache Beam |
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