ML Studio vs Workbench
Kyle and Joseph discuss the differences between ML Studio and Workbench, highlighting the choice between simple serverless development and a more advanced server-based model. They also touch on the flexibility of using docker containers and the ability to integrate ML into other applications.In this clip
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Data Skeptic
Data science tools and other announcements from Ignite
Related Questions
What are the ways to deploy AI models as discussed in the episode Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17 and the clip Continuous Delivery Insights, as well as in the episode MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3 and the clip Manual ML Processes?
What are the ways to deploy AI models as discussed in the episode MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3 and the clip Manual ML Processes?
What are the ways to deploy AI models as discussed in the episode Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17 and the clip Deployment Challenges?