Improving with MLOps: 3 Steps To Operationalize at Scale
The rapid integration of model-based machine learning and AI technologies within large enterprises highlights the need for effective deployment of these models in production to maximize their advantages. However, achieving this on a large scale presents novel challenges. The concept of MLOps emerges as a solution, encompassing the standardization and optimization of the machine learning lifecycle management process. Unlike a mere adaptation of DevOps and DataOps principles, MLOps addresses the distinctive complexities of managing machine learning models in real-world settings.
During this session, our speaker Alex Aung, Director Sales Engineer underscores three pivotal reasons for the intricacies in scaling machine learning lifecycles: the presence of numerous dependencies stemming from evolving data and shifting business requirements, the diversity in language and tools among stakeholders encompassing business, data science, and IT teams, and the contrast between the specialized skills of data scientists in model creation and evaluation and the demands of software and application development.