Continual learning allows machine learning models to learn new tasks over time without forgetting previous ones, improving performance and efficiency. It supports large-scale data processing with in-memory computing for faster applications. Machine learning pipelines with continual learning can include automated model deployment, hyperparameter optimization, retraining triggers, and labeling at scale.

2. Introduction - Yochay & cnvrg.io
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• cnvrg.io
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3. Introduction - Nikil & Scale AI
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• Scale AI
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4. agenda
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5. Introduction to Continual Learning
• analytics engine for large-scale data processing
• Faster processing speed of applications due to In-memory cluster computing (100x
improvement)
• Support different workloads – batch, iterative, streaming, interactive SQL etc.
• Support multiple languages and different environments

6. ML pipeline with Continual Learning

7. Why do we need continual learning?

8. Why do we need continual learning?

9. ML pipeline with Continual Learning

10. AutoML and Hyperparamaters Optimization
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11. AutoML and Hyperparamaters Optimization
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12. Automatic & safe model deployments
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13. Trigger retraining
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14. Labeling @ Scale
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15. Summary
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