***Due to unforeseen circumstances, this webinar has been canceled and will not be rescheduled.***
Machine learning (ML) as a technology has been around for years, beginning with Arthur Samuel’s pioneering work at IBM in 1952 where he helped the computer improve with each game of checkers it played. But despite this lineage, and that ML is no longer the luxury of research institutes or technology giants, and the ability to deploy new models remains a challenge. In fact, the pipeline to deploy new models can take months with many models never making it to production.
New solutions and best practices are coming onto the market to address these problems. The technological void that exists when data scientists want to implement machine learning can be closed when we understand and applying DevOps methods to machine learning (MLOps)
Cognilytica analysts Kathleen Walch and Ron Schmelzer will discuss the market for MLOps including findings from our most recent report on this subject. We’ll also discuss why it’s important for organizations and agencies to manage their models once in production and insights into how to go about doing this. Subject matter experts at DotScience will then discuss what is MLOps, how it has developed and where it is evolving.