The Future of MLOps and How Did We Get Here?
The Future of MLOps and How Did We Get Here?

Abstract: 

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.

Thankfully, we have created 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)

This talk we will discuss what is MLOps, how it Dotscience is involved in its evolution

3 key takeaways
• Current and emerging trends to understand in MLOps, data science, and machine learning
• The coming challenges and opportunities around MLOps
• How Dotscience can help shape your strategy today

Bio: 

As vice president of operations at Dotscience, Chris Sterry works with a team of passionate data scientists and engineers working to empower ML and data science teams through MLOps. In his diverse startup focused background, Chris's teams pushed the boundaries within MLOps, data, analytics, and next-generation technologies.