Controlling Large Data With Artificial Intelligence
| Course ID : | MAN504 |
| Course Duration : | 5 Days |
| City : | Abu Dhabi |
| Location : | UAE |
Introduction
In today's digital era, the volume of data generated is growing exponentially. Managing and analyzing such vast datasets is beyond the capabilities of traditional systems, making Artificial Intelligence (AI) an essential tool. AI offers innovative methods to handle large data efficiently, ensuring accurate insights, real-time processing, and intelligent decision-making. This topic explores how AI facilitates data control, addressing challenges like scalability, security, and data quality while leveraging cutting-edge technologies such as machine learning, deep learning, and natural language processing.
Objectives
• Understand the Role of AI in Data Management
• Explore how AI technologies are applied to handle and analyze large datasets effectively.
• Identify Challenges in Large Data Handling
• Examine common issues like data silos, unstructured formats, and scalability constraints.
• Evaluate AI Tools and Techniques
• Learn about key AI methodologies such as machine learning algorithms, data clustering, and predictive analytics.
• Highlight Use Cases and Applications
• Discover real-world scenarios where AI has revolutionized large data management in industries such as healthcare, finance, and marketing.
• Explore Ethical and Security Considerations
• Assess the ethical implications and best practices for securing sensitive data using AI.
Outlines
• Introduction to Large Data
• Definition and characteristics of large data (Volume, Velocity, Variety, Veracity, and Value).
• Challenges in traditional data management systems.
• Role of Artificial Intelligence in Data Control
• AI techniques for data preprocessing and transformation.
• Machine learning for pattern recognition and predictive modeling.
• Real-time data analysis with AI.
• AI Tools for Large Data Management
• Overview of popular AI platforms (e.g., TensorFlow, PyTorch, Apache Spark).
• Algorithms for classification, clustering, and anomaly detection.
• Applications of AI in Handling Large Data
• Case studies in industries such as healthcare (diagnostics), finance (fraud detection), and retail (customer personalization).
• Ethical and Security Considerations
• AI in maintaining data privacy and addressing biases.
• Secure storage and processing of large datasets.
• Future Trends in AI and Large Data Management
• Emerging technologies like quantum computing and advanced neural networks.
• The role of AI in the evolving landscape of big data.