Exploring IDEs, Model Deployment, and Version Control Challenges for Data Scientists

Model deployment, data warehouse options for running models, and how to best leverage BI tools: Harry Glaser and Jon Krohn discuss Modelbit’s capabilities to automate ML models from notebooks into production-ready models, reducing the time and effort in ‘translating’ information from one mode to another. Harry’s conversation with host Jon Krohn expanded on the importance of automating this task, and how developments in ML modeling have widened access to entire teams to analyze data, whatever their level of expertise.

This episode is brought to you by the AWS Insiders Podcast. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What the modern data stack is [03:28]
• Version control for data scientists [13:30]
• CI/CD, load balancing and logging [20:38]
• Snowflake vs. Redshift [30:10]
• How tools like Looker and Tableau help monitor models [35:26]

Additional materials: www.superdatascience.com/699