
573: Automating ML Model Deployment
Super Data Science: ML & AI Podcast with Jon Krohn
00:00
Tools, Hiring Practices, and the Future of Data Science
The chapter covers a range of topics including tools commonly used in daily tasks such as PyCharm, Jupyter Notebooks, and productivity tools like Notion and Lever. It delves into the advantages and disadvantages of Google inbox and the importance of engineers understanding data science concepts. The conversation concludes with insights on the future of data science, focusing on automation, data literacy, and the evolving role of data scientists in deploying ML models.
Play episode from 45:23
Transcript


