CI/CD for Machine Learning

Presented by

Yochay Ettun

About this talk

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 an automated and continuous training and 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 Yochay Ettun as he brings 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. 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 performance

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Learn from various data science and engineering experts about key topics for successful machine learning. will provide you with hands-on tutorials and workshops about the top methods in data science team management, and MLOps getting you from research to production. Stay ahead with the latest developments in auto-adaptive machine learning and CI/CD for machine learning. Learn the latest methods for machine learning model management and deployment with open source tools. Find answers on how to enhance team collaboration in your data science department, and smoothly bridge science to engineering.