How to use continual learning in your ML models, June 19 Webinar
This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.
Academics and practitioners alike believe that continual learning (CL) is a fundamental step towards artificial intelligence. CL is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in. The idea of CL is to mimic humans ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. CL of models in production will improve accuracy, and bring AI one step closer to real human intelligence.
Join data science expert and CEO of cnvrg.io Yochay Ettun as he discusses continual learning in production. This webinar will examine continual learning, and help you apply continual learning into your production models using tools like Tensorflow, Kubernetes, and cnvrg.io. This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.
Key takeaways include:
- Understanding of Continual Learning
- Optimizing for accuracy with CL
- How to use TensorFlow to apply CL
- How to make automatically adaptive machine learning
- Adapting to shifting data distributions
- Coping with outliers
- Retraining in production
- Adapting to new tasks
- Deploying ML pipeline to production
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