I’m your friendly Devtalk bot 
Thank you to all of you who participated in our Spotlight AMA!
This is now closed and all of those who commented above have been entered into the draw - meaning we’re now ready to pick a winner!
The process will be initiated when somebody clicks the special link below:
Devtalk - Dev forum at Devtalk - the forum for developers! 
Don’t be shy, we need one of you to help make the magic happen! 
Thank you for initiating the draw process…
Entering the following members into the draw…
And the winner is…
Drum roll…
Congratulations @lockejan you are the chosen one!! We’ll be in touch about your prize via PM soon 
Thank you everyone who entered, and of course @RebeccaSkinner for taking part in our Spotlight - thank you! 
Amazing. Looking forward to diving into it. 
Congratulations @lockejan ! I’ll be PMing you in a moment about your prize.
Hi Rebecca,
Would you mind sharing your thoughts on Haskell in relation machine learning and AI. I have read some people saying that it won’t cut it because of so few libraries etc and haskell is way to hard to debug, but then I have also seen some saying that it has some awesome qualities that will make it more popular in ML in the future. IIRC (paraphrase) one guy wrote that because of some intrinsic qualities of haskell either ML will go more towards haskell or other languages will steal these haskell features.
Do you have an opinion on this?
That’s a great question, and the answer is pretty nuanced I think.
At a high level, I’d say that today Haskell is probably more widely used for data engineering than for implementing models, Haskell’s data modeling capabilities and strong support for streaming data make it a great fit for a lot of data engineering work, and if I were trying to look for the best way to introduce Haskell to an ML team, I’d probably start there.
When it comes to implementing models, I think the choice is a bit more nuanced. There’s a lot Haskell brings to the table. Being able to model your data accurately and working with the type system to help you write correct code is a great benefit to building models since it makes it much easier to reason about how your models are being built. Even more importantly, being a pure functional language Haskell can give you some really amazing guarantees when you want to be able to understand and describe your system. Being able to understand how your models produce a decision, and being able to reproduce those results or test new models against historic data are huge problems that come up in any sort of data team, and Haskell allows us to model these constraints and guarantee we’re clearly capturing our inputs and outputs.
There are some challenges that come with using Haskell for writing models too. Library support is a consideration, but I often remind people to not worry about all of the libraries you don’t have, and just look to see if there are the specific libraries you need. If you’re comparing Haskell to something built largely by gluing together pieces of SciKit or Pandas or something then the library support will probably be a bit disappointing, but if you’re implementing most of your model yourself then Haskell’s going to compare a lot more favorably. Another challenge I see people talk about when using Haskell to build models is the fact that it’s often not as ergonomic to work with Haskell early in the process of developing a model. Optics give us the tools we need to arbitrarily traverse deeply nested data structures in an ad-hoc way, and more advanced type system features like GADTs can help us write code that we might use dataframes for in python, but getting to the point where you can use those things comfortably can involve a bit of an upfront investment, and it’s sometimes hard to convince people who are comfortable with Python that it’s going to be worth it.
On the whole, I think Haskell can be a great choice, but it’s also a particularly hard sell to data scientists in my experience. I’d certainly encourage you to consider trying Haskell for your own problems, and certainly look at it for data engineering problems, but for building models it’s probably best to work with your team and help them get comfortable with Haskell before you start trying to build significant work with it.
Thanks for the detailed answer! Personally I am almost half way through the book now. This gave me some ideas to think about.
