MLOps Simplified
Working at a small company requires a versatile approach to data science, encompassing everything from heavy-duty ETL to model deployment and monitoring. It's essential to grasp MLOps well enough to set up simple batch and real-time inference pipelines, rather than overcomplicating the process with specialized techniques. The landscape is changing, and the next generation should aspire to be open-source developers, reflecting the evolving nature of technology and innovation.In this clip
From this podcast
Practical AI
Machine learning at small organizations
Related Questions
Do companies need large machine learning teams, as discussed in the episode MLOps Coffee Sessions #13 How to Choose the Right Machine Learning Tool: A Conversation // Jose Navarro and Mariya Davydova, and the clip ML Infrastructure Standardization?
Do companies need large machine learning teams, as discussed in the episode MLOps Coffee Sessions #13 How to Choose the Right Machine Learning Tool: A Conversation // Jose Navarro and Mariya Davydova and the clip ML Infrastructure Standardization?