15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- AI Engineers
- Data Scientists
- Machine Learning Engineers
- Software Developers
- Data Architects
- Anyone needing generative AI for data engineering skills
Session Objectives
- Understand the fundamentals of generative AI for data engineering.
- Master data synthesis and augmentation using generative models.
- Utilize AI for automated code generation and data pipeline development.
- Implement AI-driven data quality checks and anomaly detection.
- Design and build intelligent data transformation workflows.
- Optimize data pipelines with AI-powered automation.
- Troubleshoot and address common issues in AI-assisted data engineering.
- Implement AI-driven metadata management and data discovery.
- Integrate generative AI with various data engineering tools and platforms.
- Understand how to handle large-scale AI-assisted data processing.
- Explore advanced generative AI techniques for data engineering (e.g., reinforcement learning for pipeline optimization).
- Apply real world use cases for generative AI in data engineering tasks.
- Leverage generative AI tools and frameworks for efficient data engineering.
About the Course
Revolutionize your data engineering workflows with our Generative AI for Data Engineering Training Course. This program is designed to equip you with the essential skills to use generative AI to assist in data engineering tasks, enabling you to automate and optimize data pipelines with cutting-edge AI techniques. In today's rapidly evolving data landscape, mastering generative AI for data engineering is crucial for organizations seeking to enhance efficiency and innovation. Our generative AI training course offers hands-on experience and expert guidance, empowering you to leverage AI to transform data engineering practices.
This AI-powered data pipelines training delves into the core concepts of generative AI in data engineering, covering topics such as data synthesis, automated code generation, and AI-driven data quality checks. You'll gain expertise in using industry-standard tools and techniques to use generative AI to assist in data engineering tasks, meeting the demands of modern data-intensive organizations. Whether you're a data engineer, AI engineer, or data scientist, this Generative AI for Data Engineering course will empower you to build and manage intelligent data systems.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of generative AI for data engineering.
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Subtopic 1.2:
Overview of data synthesis, code generation, and AI-driven quality checks.
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Subtopic 1.3:
Setting up a generative AI development environment.
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Subtopic 1.4:
Introduction to generative AI tools and frameworks.
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Subtopic 1.5:
Best practices for AI-assisted data engineering.
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Subtopic 2.1:
Mastering data synthesis and augmentation using generative models.
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Subtopic 2.2:
Utilizing generative adversarial networks (GANs) and variational autoencoders (VAEs).
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Subtopic 2.3:
Implementing synthetic data generation for testing and training.
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Subtopic 2.4:
Designing and building data augmentation pipelines.
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Subtopic 2.5:
Best practices for data synthesis.
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Subtopic 3.1:
Utilizing AI for automated code generation and data pipeline development.
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Subtopic 3.2:
Implementing AI-driven SQL and Python code generation.
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Subtopic 3.3:
Designing and building automated pipeline generation tools.
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Subtopic 3.4:
Optimizing code generation for efficiency.
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Subtopic 3.5:
Best practices for code generation.
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Subtopic 4.1:
Implementing AI-driven data quality checks and anomaly detection.
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Subtopic 4.2:
Utilizing machine learning for data validation.
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Subtopic 4.3:
Designing and building anomaly detection systems.
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Subtopic 4.4:
Optimizing quality checks for data integrity.
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Subtopic 4.5:
Best practices for data quality.
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Subtopic 5.1:
Designing and building intelligent data transformation workflows.
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Subtopic 5.2:
Utilizing AI for data cleaning and preprocessing.
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Subtopic 5.3:
Implementing automated data integration and ETL processes.
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Subtopic 5.4:
Optimizing workflows for data accuracy.
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Subtopic 5.5:
Best practices for data transformation.
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Subtopic 6.1:
Optimizing data pipelines with AI-powered automation.
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Subtopic 6.2:
Utilizing reinforcement learning for pipeline optimization.
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Subtopic 6.3:
Implementing AI-driven resource management.
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Subtopic 6.4:
Designing efficient AI-assisted pipelines.
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Subtopic 6.5:
Best practices for pipeline optimization.
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Subtopic 7.1:
Troubleshooting and addressing common issues in AI-assisted data engineering.
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Subtopic 7.2:
Analyzing AI model errors and pipeline failures.
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Subtopic 7.3:
Utilizing problem-solving techniques for resolution.
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Subtopic 7.4:
Resolving common AI-driven data errors.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing AI-driven metadata management and data discovery.
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Subtopic 8.2:
Utilizing natural language processing (NLP) for metadata extraction.
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Subtopic 8.3:
Designing and building AI-driven data catalogs.
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Subtopic 8.4:
Optimizing metadata for data discovery.
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Subtopic 8.5:
Best practices for metadata management.
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Subtopic 9.1:
Integrating generative AI with various data engineering tools and platforms.
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Subtopic 9.2:
Utilizing APIs and data connectors.
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Subtopic 9.3:
Implementing AI-driven tasks within existing data pipelines.
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Subtopic 9.4:
Optimizing integration for data processing.
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Subtopic 9.5:
Best practices for integration.
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Subtopic 10.1:
Understanding how to handle large-scale AI-assisted data processing.
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Subtopic 10.2:
Utilizing distributed AI models for data processing.
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Subtopic 10.3:
Implementing data sharding and parallel processing.
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Subtopic 10.4:
Designing scalable AI-driven data architectures.
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Subtopic 10.5:
Best practices for large scale data.
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Subtopic 11.1:
Exploring advanced generative AI techniques for data engineering (reinforcement learning for pipeline optimization).
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Subtopic 11.2:
Utilizing reinforcement learning for pipeline resource allocation.
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Subtopic 11.3:
Implementing advanced AI models for data synthesis.
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Subtopic 11.4:
Designing and building advanced AI-driven solutions.
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Subtopic 11.5:
Optimizing advanced techniques for specific applications.
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Subtopic 11.6:
Best practices for advanced techniques.
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Subtopic 12.1:
Implementing generative AI for synthetic data generation in testing.
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Subtopic 12.2:
Utilizing AI for automated ETL pipeline generation.
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Subtopic 12.3:
Implementing AI-driven data quality monitoring in real-time.
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Subtopic 12.4:
Utilizing AI for metadata enrichment and data discovery.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Utilizing generative AI tools and frameworks (TensorFlow, PyTorch).
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Subtopic 13.2:
Implementing AI-driven pipelines with specific tools.
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Subtopic 13.3:
Designing and building automated workflows.
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Subtopic 13.4:
Optimizing tool usage for efficient development.
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Subtopic 13.5:
Best practices for tool implementation.
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Subtopic 14.1:
Implementing AI-driven pipeline monitoring.
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Subtopic 14.2:
Utilizing AI for anomaly detection in pipeline performance.
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Subtopic 14.3:
Designing and building performance dashboards.
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Subtopic 14.4:
Optimizing monitoring for real-time insights.
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Subtopic 14.5:
Best practices for monitoring.
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Subtopic 15.1:
Emerging trends in generative AI for data engineering.
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Subtopic 15.2:
Utilizing AI for automated data mesh implementation.
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Subtopic 15.3:
Implementing AI for data governance and compliance.
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Subtopic 15.4:
Best practices for future applications.
