Understanding the 3 Vs of MLOps
Despite the growing interest in applied machine learning, organizations continue to face enormous challenges in integrating ML into real-world applications. A considerable percentage of machine learning projects either get abandoned before they’re finished or fail to deliver on their promises.
Applied ML is a young and evolving area. MLOps, an emerging field of practices for deploying and maintaining machine learning models, has inspired many tools and platforms for ML pipelines. However, much remains to be done.
A recent study by scientists at the University of California, Berkeley, sheds light on best practices for operationalizing machine learning in different organizations. The paper, which is based on a survey of machine learning engineers in various industries, contains vital lessons for the successful deployment and maintenance of ML models, and guidance for the development of future MLOps tools.
Read the full article on TechTalks.
For more on AI research: