Our guests helped create a ML pipeline that enabled image processing and automated image comparisons, enabling healthcare use cases through their series of microservices that automatically detect, manage, and process images received from OEM equipment.

In this episode they will chat through the challenges and how they overcame them, focusing specifically on the wait strategy for their ML Pipeline Healthcare Solution microservices. We’ll also touch on how improvements were made to an open source Go package as part of this project.

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Featuring:

Neethu Elizabeth Simon – LinkedIn
Samantha Coyle – Twitter
Angelica Hill – Twitter, GitHub

Show Notes:

Something missing or broken? PRs welcome!

Timestamps:

(00:00) - It's Go Time!
(00:44) - Introducing the guests
(05:38) - The problem that needed solving
(07:57) - Why Go?
(11:17) - The Go implementation
(14:08) - How to know it's the right fit
(19:53) - Getting it ready to be used
(25:02) - The core takeaways
(28:47) - Other challenges
(34:34) - The learning curve of Go
(36:57) - Would you choose Go again?
(38:01) - Iterating, going forward
(41:49) - Unpopular Opinions!
(42:13) - Samantha's unpop
(44:32) - Neethu's unpop
(46:10) - Outro

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