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 Duration 14 hours

Course Outline

Foundations of Continual Learning

  • The significance of continual learning
  • Challenges in sustaining fine-tuned models
  • Core methodologies and learning paradigms (online, incremental, transfer)

Data Management and Stream Processing

  • Handling dynamic datasets
  • Online learning utilizing mini-batches and streaming interfaces
  • Challenges in data labeling and annotation over extended periods

Guarding Against Catastrophic Forgetting

  • Elastic Weight Consolidation (EWC)
  • Replay techniques and rehearsal protocols
  • Regularization methods and memory-enhanced networks

Model Drift Analysis and Surveillance

  • Identifying data and concept drift
  • Key indicators for model health and performance degradation
  • Initiating automated model refinements

Automating Model Refinements

  • Automated retraining schedules and strategies
  • Seamless integration with CI/CD and MLOps processes
  • Oversight of update frequency and rollback procedures

Continual Learning Ecosystems and Toolkits

  • Introduction to Avalanche, Hugging Face Datasets, and TorchReplay
  • Platform capabilities for continual learning (e.g., MLflow, Kubeflow)
  • Scalability and deployment factors

Practical Applications and System Designs

  • Predicting customer behavior amidst shifting patterns
  • Industrial machine surveillance with progressive enhancements
  • Fraud detection systems adapting to evolving threat landscapes

Recap and Future Directions

Requirements

  • Familiarity with machine learning workflows and neural network structures
  • Practical experience in model fine-tuning and deployment pipelines
  • Proficiency in data versioning and managing the model lifecycle

Target Participants

  • AI maintenance engineers
  • MLOps engineers
  • Machine learning specialists overseeing model lifecycle continuity

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