Program Structure

Module 1: Introduction to Streaming Data

• What is streaming data and why is it important for real-time decision making?
• Challenges in processing and analyzing large streams of data.
• Overview of popular streaming data frameworks: Apache Kafka, Apache Flink, and others.

Module 2: Data Ingestion and Streaming Frameworks

• Setting up and managing streaming data sources using Apache Kafka.
• How to configure and run Kafka producers and consumers to stream data.
• Introduction to Apache Flink for processing real-time data streams.
• Hands-on project: Set up a streaming pipeline using Kafka to ingest data from real-time sources.

Module 3: Data Processing with Apache Flink

• Core concepts of Apache Flink: data streaming, windowing, time management, and event processing.
• Building and managing Flink jobs for real-time data processing.
• Data aggregation, transformation, and enrichment in real-time streaming pipelines.
• Hands-on project: Develop a data processing pipeline using Flink for real-time analytics.

Module 4: Integrating AI with Streaming Data

• How AI models are integrated with streaming data pipelines for real-time predictions.
• Techniques for applying machine learning and deep learning algorithms to streaming data.
• Using TensorFlow and PyTorch with streaming data for real-time inference.
• Hands-on project: Integrate a simple AI model into a streaming data pipeline to perform predictions in real-time.

Module 5: Real-Time Analytics and Dashboarding

• Building real-time dashboards to monitor and visualize streaming data insights.
• Techniques for visualizing large volumes of real-time data, including time-series charts and anomaly detection.
• Using tools like Grafana and Kibana for streaming data analytics and visualization.
• Hands-on project: Create a real-time dashboard for monitoring AI predictions and data streams.

Module 6: Deploying AI in Real-Time Data Pipelines

• How to deploy AI models into production environments with streaming data pipelines.
• Ensuring scalability and fault tolerance in real-time AI models using containerization and orchestration tools like Docker and Kubernetes.
• Managing model updates and continuous learning in production environments.
• Hands-on project: Deploy a real-time AI model in a containerized environment using Docker and Kubernetes.

Module 7: Stream Processing in Distributed Environments

• How to scale streaming data processing using distributed systems and cloud platforms (e.g., AWS, Google Cloud, Azure).
• Optimizing performance and minimizing latency in real-time data pipelines.
• Managing stateful stream processing applications and handling large-scale data in a distributed environment.

Module 8: Advanced Topics in Streaming Data and AI

• Advanced stream processing techniques: complex event processing (CEP), anomaly detection, and pattern recognition in real-time data.
• Introduction to Edge AI and deploying models to edge devices for real-time decision-making in IoT applications.
• Exploring the role of 5G and low-latency networks in streaming data processing and AI applications.

Final Project

• Build an end-to-end real-time streaming data application with AI-based insights.
• Design and implement a solution that ingests, processes, analyzes, and visualizes streaming data in real-time using AI models.
• Example projects: Real-time fraud detection system, predictive maintenance for IoT devices, or anomaly detection in financial transactions.