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Non academic functional Workflows
- 1. This talk is a thanks from us to Typelevel Digital Innovation Partners Non-academic Functional Workflows
- 2. Agenda • Project Introduction • “Architecture” and where Typelevel projects shined
- 3. From the image to the insight https://www.capta-astra.com/ In order to address the fragmented software solution landscape in microscopy, ZEISS took initiative to develop a platform that enables easy software workflow development and deployment.
- 4. DSL – Glue - Runner
- 5. DSL – Glue - Runner
- 6. Cats: Free & State Monix: Task Shapeless: HList & labelled Generic
- 8. Personal Highlights • Understand the concept and you are ready to go. • Scala exercises, to check if you really understood.
Editor's Notes
- Team: 1 Scala – 2 CS SIP: Support Unit providing help with prototype to MVP – not all present in product
- A light get out dance Where TL helped How
- Insight not the Image interesting Cancer or not Functional: know more about the stuff from other running on our system => max freedom inside the modules restrict on combination Not starting from zero made it possible for us to come up with a prototype within weeks -> later
- DSL – (AST) User defined Workfows Glue – Marshalling Runner – start right function with right inputs
- In our case containers
- Cats: - All the compiler errors - Time machine by serialized state : Lookup not in the Runner – side effective modules Shapeless: - Marshalling by instanciating case classes to make type verification happen Monix: - Fallback - Parallel
- Cool Fact we could compile everything to Scaljs on use it on a Raspberry for a coffe machine cleaning ml workflow.