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I have spent the past two decades developing software professionally, solving some of the most intricate engineering problems across a variety of business domains. When I started re-discovering ML a few years ago to address some use cases around text / language processing (my prior experience with ML was during my engineering days nearly 20 years ago), I discovered that the tooling around ML, more specifically around integrating NLP workflows into an agile engineering team's workflow needed significant rejig. Even though the tooling has been getting better, I still feel we haven't reached a level of ease today as compared to say the level with which an engineering team can introduce rigor within their DevOps cycle. The typical developer in an agile team faces quite an uphill task if they need to do NLP. More so if they need to integrate NLP notions into their dev/test/deploy loop. The sheer number of frameworks, libraries and tools and then the plumbing and interfacing required to get them to a usable state is often slow and re-invented every single time by every team.
Our intent behind Astutic AI was to further the state of the art in a developer focused tooling around ML starting with addressing some of these challenges in NLP/NER. Over the course of next few blogs, I will spend some time discussing these in more depth. Thank you for reading, and feel free to drop me a note.