The MLOps community has grown dramatically recently, with security, a data-centric approach, ethical implications, and a growing and diverse community rising in 2021. In this episode, MLOps Community managers Demetrios Brinkmann and David Aponte join Steph Locke and Stephen Foskett to discuss what has changed over the last year. It seems that a new ML company is launching every week, and the MLOps Community provides a great way to learn about these. We are also seeing a push and pull between open source and cloud platforms, and concern about lock-in and technical debt. Data science and machine learning are merging, with greater focus on data quality and quantity when training models.
- Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?