Summary
Every business develops their own specific workflows to address their internal organizational needs. Not all of them are properly documented, or even visible. Workflow automation tools have tried to reduce the manual burden involved, but they are rigid and require substantial investment of time to discover and develop the routines. Boaz Hecht co-founded 8Flow to iteratively discover and automate pieces of workflows, bringing visibility and collaboration to the internal organizational processes that keep the business running.
Announcements
Parting Question
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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Every business develops their own specific workflows to address their internal organizational needs. Not all of them are properly documented, or even visible. Workflow automation tools have tried to reduce the manual burden involved, but they are rigid and require substantial investment of time to discover and develop the routines. Boaz Hecht co-founded 8Flow to iteratively discover and automate pieces of workflows, bringing visibility and collaboration to the internal organizational processes that keep the business running.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Boaz Hecht about using AI to automate customer support at 8Flow
- Introduction
- How did you get involved in machine learning?
- Can you describe what 8Flow is and the story behind it?
- How does 8Flow compare to RPA tools that companies are using today?
- What are the opportunities for augmenting or integrating with RPA frameworks?
- What are the key selling points for the solution that you are building? (does AI sell? Or is it about the realized savings?)
- What are the sources of signal that you are relying on to build model features?
- Given the heterogeneity in tools and processes across customers, what are the common focal points that let you address the widest possible range of functionality?
- Can you describe how 8Flow is implemented?
- How have the design and goals evolved since you first started working on it?
- What are the model categories that are most relevant for process automation in your product?
- How have you approached the design and implementation of your MLOps workflow? (model training, deployment, monitoring, versioning, etc.)
- What are the open questions around product focus and system design that you are still grappling with?
- Given the relative recency of ML/AI as a profession and the massive growth in attention and activity, how are you addressing the challenge of obtaining and maximizing human talent?
- What are the most interesting, innovative, or unexpected ways that you have seen 8Flow used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on 8Flow?
- When is 8Flow the wrong choice?
- What do you have planned for the future of 8Flow?
Parting Question
- From your perspective, what is the biggest barrier to adoption of machine learning today?
- Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Support The Machine Learning Podcast
