ML Workflow Insights
The discussion highlights the critical role of ETL in both database systems and machine learning workflows, emphasizing how innovations in data handling have evolved. Arun points out the complexities of ML Ops compared to traditional data operations, while drawing parallels between model building systems and querying tools in databases. The conversation encourages a broader perspective on the entire ML application lifecycle, stressing the need for comprehensive tools to address emerging bottlenecks in machine learning adoption.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
The New DBfication of ML/AI with Arun Kumar - #553
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