15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Machine Learning Engineers
- AI Researchers
- Data Scientists
- Deep Learning Practitioners
- Software Developers
- Anyone needing deep learning data engineering skills
Session Objectives
- Understand the fundamentals of data engineering for deep learning.
- Master large-scale data processing and storage for deep learning.
- Utilize feature extraction and transformation techniques for deep learning models.
- Implement data augmentation and preprocessing for complex datasets.
- Design and build efficient data pipelines for deep learning training.
- Optimize data pipelines for GPU-accelerated training.
- Troubleshoot and address common issues in deep learning data preparation.
- Implement data versioning and reproducibility for deep learning projects.
- Integrate data pipelines with various deep learning frameworks.
- Understand how to handle unstructured data (images, text, audio) for deep learning.
- Explore advanced data engineering techniques for deep learning (e.g., distributed training, data sharding).
- Apply real world use cases for data engineering in deep learning projects.
- Leverage data engineering tools an
About the Course
Fuel high-performance neural networks with our Data Engineering for Deep Learning Training Course. Tailored for data engineers, ML engineers, and AI research specialists, this hands-on program equips technical teams to architect scalable data pipelines tailored specifically for complex deep learning workloads. Guided by AI infrastructure experts, you will master large-scale processing of unstructured data (image, text, and audio), distributed data loaders, GPU memory optimization, automated feature extraction, and dataset augmentation—enabling your team to minimize model training latency and accelerate deployment into production.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data engineering for deep learning.
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Workshop 1.2:
Overview of large-scale data processing and feature engineering.
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Workshop 1.3:
Setting up a deep learning data engineering development environment.
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Workshop 1.4:
Introduction to data engineering tools and frameworks.
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Workshop 1.5:
Best practices for deep learning data engineering.
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Workshop 2.1:
Mastering large-scale data processing and storage for deep learning.
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Workshop 2.2:
Utilizing distributed file systems and data lakes.
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Workshop 2.3:
Implementing data partitioning and sharding.
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Workshop 2.4:
Designing and building efficient data storage solutions.
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Workshop 2.5:
Best practices for large-scale data.
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Workshop 3.1:
Utilizing feature extraction and transformation techniques for deep learning models.
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Workshop 3.2:
Implementing techniques for image, text, and audio data.
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Workshop 3.3:
Designing and building feature extraction pipelines.
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Workshop 3.4:
Optimizing feature sets for deep learning models.
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Workshop 3.5:
Best practices for feature extraction.
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Workshop 4.1:
Implementing data augmentation and preprocessing for complex datasets.
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Workshop 4.2:
Utilizing techniques for image augmentation and text preprocessing.
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Workshop 4.3:
Designing and building data augmentation pipelines.
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Workshop 4.4:
Optimizing preprocessing for model performance.
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Workshop 4.5:
Best practices for data augmentation.
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Workshop 5.1:
Designing and building efficient data pipelines for deep learning training.
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Workshop 5.2:
Utilizing data pipeline frameworks (e.g., TensorFlow Data API, PyTorch DataLoader).
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Workshop 5.3:
Implementing automated data preprocessing workflows.
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Workshop 5.4:
Optimizing pipelines for GPU-accelerated training.
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Workshop 5.5:
Best practices for data pipelines.
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Workshop 6.1:
Optimizing data pipelines for GPU-accelerated training.
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Workshop 6.2:
Utilizing data batching and caching techniques.
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Workshop 6.3:
Implementing data prefetching and asynchronous loading.
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Workshop 6.4:
Designing efficient data loading strategies.
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Workshop 6.5:
Best practices for GPU training.
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Workshop 7.1:
Debugging common issues in deep learning data preparation.
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Workshop 7.2:
Analyzing data loading and preprocessing errors.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common data preparation errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data versioning and reproducibility for deep learning projects.
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Workshop 8.2:
Utilizing data version control systems (e.g., DVC).
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Workshop 8.3:
Implementing data lineage tracking.
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Workshop 8.4:
Designing reproducible data pipelines.
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Workshop 8.5:
Best practices for data versioning.
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Workshop 9.1:
Integrating data pipelines with various deep learning frameworks.
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Workshop 9.2:
Utilizing TensorFlow, PyTorch, and other frameworks.
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Workshop 9.3:
Implementing data loaders and datasets for specific frameworks.
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Workshop 9.4:
Optimizing integration for model training.
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Workshop 9.5:
Best practices for framework integration.
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Workshop 10.1:
Understanding how to handle unstructured data (images, text, audio) for deep learning.
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Workshop 10.2:
Utilizing techniques for data parsing and embedding.
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Workshop 10.3:
Implementing data preprocessing for unstructured formats.
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Workshop 10.4:
Designing data pipelines for unstructured data.
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Workshop 10.5:
Best practices for unstructured data.
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Workshop 11.1:
Exploring advanced data engineering techniques for deep learning (distributed training, data sharding).
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Workshop 11.2:
Utilizing distributed data loading strategies.
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Workshop 11.3:
Implementing data sharding for large datasets.
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Workshop 11.4:
Designing and building advanced data pipelines.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing data engineering for computer vision applications.
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Workshop 12.2:
Utilizing data engineering for natural language processing tasks.
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Workshop 12.3:
Implementing data engineering for audio processing.
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Workshop 12.4:
Utilizing data engineering for recommendation systems.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing data engineering tools and libraries (Apache Spark, TensorFlow Data API).
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Workshop 13.2:
Implementing data pipelines with specific tools.
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Workshop 13.3:
Designing and building automated data processing scripts.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing data performance monitoring.
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Workshop 14.2:
Utilizing data loading and preprocessing metrics.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for data pipeline efficiency.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in deep learning data engineering.
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Workshop 15.2:
Utilizing AI for automated data preprocessing.
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Workshop 15.3:
Implementing data engineering for federated learning.
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Workshop 15.4:
Best practices for future applications.
