In episode 35 of the Acceleration Economy Minute, Kieron explains what machine learning operations (MLOps) are, given the context of SAS’s recent achievement.
Highlights
00:15 — SAS, an artificial intelligence (AI), analytics, and data management organization, has just been named the leader in the inaugural IDC Marketscape Worldwide Machine Learning Platforms of 2022 Vendor Assessment — a big victory for the company in terms of its MLOps.
00:46 — ‘Machine learning operations’ is a term being used more frequently, especially in hyperautomation, although it is strikingly different from actual machine learning (ML).
00:59 — MLOps are a core function of ML engineering that focuses on the streamlining of machine learning model processes. Essentially, MLOps are a set of best practices for enabling the management of ML projects. These operations put ML systems and technologies into practice.
01:33 — The notion of MLOps is about deploying the models that have been developed. In many cases, there are dedicated MLOps engineers who carry out this deployment. MLOps are a way of ensuring consistency and producing high-quality ML models.
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