INSUBCONTINENT EXCLUSIVE:
to or being forced to go back and forth between disparate developer tools.Under the hood, Meeshkan says it uses patent-pending tech for
speedy partitioning of data-flow across distributed infrastructure
checkpointing of ML models in TensorFlow and PyTorch.In an email exchange, Meeshkan founder Mike Solomon explained that training ML models
is currently done through command line interfaces and web dashboards, which is not optimum for collaboration
This is because teams typically need to communicate about ML model training, make decisions about models, act on these decisions instantly
In unit testing, this could be covering the corner case of an API that returns null values in certain circumstances
What unites these scenarios is that developers are dealing with externalities, like data or a third-party API, and trying to build fast on
A world-class IDE, while it helps with lots of problems, does not provide much value for these small tweaks
fixing this, Solomon tells me that Meeshkan set out to create a bot on Slack that helps teams monitor and tweak the training of their ML
things like changing a learning rate or a batch size into action, right from Slack
From this simple idea, the floodgates opened
Developers really quickly let us know what they wanted to control from Slack, some of which is trivial to implement, some of which is
developing a suite of products that address this concern.This includes a second product called unmock.io, which brings the same idea to
testing and continuous integration and has seen traction at AWS re:Invent
