Machine learning is rarely a linear process that magically produces results, and iterating between your models and your data will ensure a solid foundation to build your custom ML solutions on. Lj and I created a spaCy project to showcase the functionality of the SpanRuler within a NER pipeline, but when we didn't see the improvement we were looking for in the initial pipeline evaluation, I looked into the data and found some inconsistencies in the annotations. This led me to go back with a new annotation workflow and improve the overall pipeline.

In the span of less than a year, I went from knowing almost nothing about natural language processing and machine learning to having a job at one of the coolest ML/NLP companies out there. I’m going to take a bit of time in this blog post to reflect on some of the things I did and mindsets I had that I think led me to where I am today.

I just somewhat finished the beta of a project (synsong) to practice NLP, Python, and API skills. Right now, you can input genre, popularity, and a natural language prompt (such as a sentence, quote, or paragraph), and it will create a playlist based on matches with the lyrics, song title, and artist name with the prompt.

Notion has a very interesting way of setting up their pages, formatting their text, and creating items, and that happens to be very different than Slack's approach. By taking examples of text from both APIs, I was able to set up a translator between Slack and Notion. This blog post walks you through how I did it!