Yes, oh yes. From what I've seen, many products we've seen fall into one of several categories: - People thinking if Jupyter notebooks had just the right CSS a

Yes, oh yes. From what I've seen, many products we've seen fall into one of several categories: - People thinking if Jupyter notebooks had just the right CSS and animations, it'd make machine learning unstuck. - People coming from web dev and thinking that ML folks are somehow unaware of Docker/CICD/Kubernetes/Git and if they could just be enlightened, they'd be much more productive. - People coming from a background of publishing blog posts/Youtube videos about machine learning, or who've worked with cute datasets who think that the actual workflow is someone giving you a CSV and a metric to optimize for à la Kaggle and building something around that workflow. This is for ML what Aikido is for fighting: it only works if your opponent knows the script and agrees to fall when you go Hayaaa! Disclosure: we're working on our machine learning platform[0] because we desperately need it. We do not come from a web dev or blogging background, and it shows. We're building it because we spent seven years actually delivering machine learning products to paying enterprise in the mid six figures contracts with actual money, trust, and careers on the line. We've done it OK enough to have companies trust us and become repeat customers, but, anyone we faced the problems that anyone who has worked on actual projects has faced. Just getting to formulate the problem is akin to extracting Ramses II's brain through his nostrils. Agreeing on the data to use and then getting the data to a usable form takes a long time. Defining the real world metric is not obvious [if only it just boiled down to F1/AUC/ROC/ETC]. Most things that matter are done by a team, and tools work great for one individual, but break for teams. Just getting results to the client makes the work an endangered specie in an "only one result out of 100 makes it to the client". It is unbelievable how much these problems impact the team. People can quit over this and we've suffered a lot. So we're building around our workflow that has generated actual revenues with very large organizations that were satisfied enough to come back for more projects. It is not a perfect workflow, but it is from the real world on real projects and real clients that paid real money to solve real problems. And we're building it for ourselves to use on actual projects. We aim for 10x improvement. [0]: https://iko.ai https://iko.ai