CI/CD (Continuous Integration/Continuous Deployment) has long been a successful process for most software applications. The same can be done with Machine Learning applications, offering automated and continuous training as well as continuous deployment of machine learning models. Using CI/CD for machine learning applications creates a truly end-to-end pipeline that closes the feedback loop at every step of the way, and maintains high performing ML models. It can also bridge science and engineering tasks, causing less friction from data, to modeling, to production and back again. Join CEO of cnvrg.io Yochay Ettun as he takes you through how to create a CI/CD pipeline for machine learning, and set up continuous deployment in just one click. With a depth of knowledge in all the latest research, Yochay will share with you today's top methods for applying CI/CD to machine learning.
Key webinar takeaways:
Configure and execute continuous training and continuous deployment for ML
Define dependencies and triggers
Automatically connect data pipeline, machine learning pipeline and deployment pipelines
Integrate model bias detection or fairness and accuracy validations
Build monitoring infrastructure to close the data feedback loop
Collect live data for improved model performances
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