Join this live webinar from cnvrg, Continual Learning with Human-in-the-loop, Nov 26 @ 12 PM EST, and learn the role of human-in-the-loop in your ML pipeline, how to close the loop in your pipeline, and much more.
Live Webinar: Continual Learning with Human-in-the-loop
November 26th, 2019 @ 12pm EST
The reality of AI applications relies on the synchronization of human and machine intelligence. By creating a continuous feedback loop between human and machines, machine learning models become smarter, more confident, and more accurate over time. Get an inside look at how data science experts are using the Human-in-the-Loop approach in sync with continual learning to build better models in production. Continual learning technology enables models to auto-adapt and retrain with new data, closing the ML loop. In order to produce high quality models, humans are critical in training, tuning and testing data and models that go into production. Join cnvrg.io and special guest Scale AI in a discussion on how the future of AI relies on the accuracy of auto-adaptive models and human intelligence to train, tune and test your models in production. You’ll sit with data science expert Yochay Ettun, and Nikil Balakrishan of Scale AI who will share the critical ways Human-in-the-Loop continual learning contribute to true artificial intelligence. Scale AI, is a high quality data labeling platform that ensures data is prepared for accurate machine learning model building. Yochay and Nikil will talk about the role humans have in continual learning and how to pair human and machine intelligence.
Key webinar takeaways:
End-to-end machine learning pipeline with continuous training and continuous deployment
The role of Human-in-the-loop in your ML pipeline
How to close the loop in your machine learning pipeline
A real example of continual learning pipeline with Kubernetes, cnvrg.io & ScaleAI
How to optimize for Human-in-the-loop CL
Unable to attend?
Register for a recording of the webinar and copy of the presentation following the live event.