MLOps: DevOps for Machine Learning

Much attention is given to machine learning model training and testing in the industry. While these activities are essential for producing a production-ready machine learning model, organizations face some critical business challenges that must be addressed when building and testing machine learning models. Things like the reproducibility of results, accuracy of predictions, reusability of components, and trackability of experimentation are all vital to the success of any application.  The term MLOps has emerged as a method for applying DevOps practices and automation to the machine learning model development, testing, and delivery process. Join Jeffery Payne as he discusses what MLOps is all about and how to leverage DevOps practices and automation to solve the business challenges discussed above. Learn how to track your end-to-end machine learning development process so it can be reproduced as needed for governance and compliance. See what is necessary to keep models accurate in production and avoid bias and unfair predictions. Understand how to build reusable pipeline components to scale your machine-learning practices. Take home valuable ideas and tools for tracking your machine-learning experiments and overall process.