#data science #data engineering #machine learning

Data Versioning

Productionizing machine learning/AI/data science is a challenge. Not only are the outputs of machine-learning algorithms often compiled artifacts that need to be incorporated into existing production services, the languages and techniques used to develop these models are usually very different than those used in building the actual service. In this post, I want to explore how the degrees of freedom in versioning machine learning systems poses a unique challenge. I'll identify four key axes on which machine learning systems have a notion of version, along with some brief recommendations for how to simplify this a bit. ...

#personal #weekly-recap

2019 Weekly Recap: Week 4

This week was wild! I had settled in for a nice week of getting some progress done on various work, extracurricular, and life things, and instead I ended up traveling to Köln on very short notice for half of the week. I really like Köln a lot, so I was happy to go. The city reminds me of Portland in a way, and I like working from my company’s office there. Because of that trip, I once again did not accomplish as much on personal tasks as I wanted. Alas. ...