Harry’s Analytics used Docker and AWS CloudFormation to provision a remote Jupyter notebook server for training machine learning models that can be spun up/torn down on demand and offers the same convenience as working locally.

Here on the Analytics team at Harry’s, we frequently found ourselves training machine learning models on data coming from the entirety of Harry’s existence (4+ years and counting!) in Jupyter notebooks on our laptops. While perhaps this was a feasible workflow in the earlier days of Harry’s when there wasn’t as much data, as our data grew it became painfully slow to build and iterate on models on our local machines.

We came to desire the ability to perform our model training on machines with more cores, more memory, and even GPUs depending on the task at hand. Further, we wanted working on a remote machine with our desired hardware specs to feel the same as working locally on our laptops in terms of convenience. …


Andrea Heyman

Software Engineer at Etsy, data science enthusiast, former mathematician

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