Published in The Startup·PinnedReady for Launch: API Deployment With FastAPI and AWSOne of the great things about learning data science at Lambda School is that after all of the sprint challenges, assessments, and code challenges, you still have to prove your acumen by working on a real-world, cross-functional project. They call this portion of the program Lambda Labs, and in my…Data Engineering8 min readData Engineering8 min read
Published in The Startup·Oct 23, 2020Troubleshooting in AWS: Elastic Beanstalk and RDSPotential steps to resolve dreaded “Degraded” and “Severe” status Working with AWS offers some useful tools for API deployment, but certainly don’t make a project immune from API-crashing bugs. …AWS5 min readAWS5 min read
Published in The Startup·Jul 30, 2020K-Nearest Neighbors With Base PythonFunctional and Object-Oriented Implementations for Classification — This post will explore two implementations of the K-Nearest Neighbors algorithm in base python (without scikit-learn), and compare classification results on the iris dataset with those of a scikit-learn implementation. Nearest neighbors is a relatively simple, but versatile algorithm that can be used for both regression and classification problems. In…Python6 min readPython6 min read
Published in Towards Data Science·May 1, 2020Fantasy Football and The MachineUsing machine learning to get better at fantasy football drafts — There’s an old adage among fantasy football players that says “you can’t win a league in the draft, but you can certainly lose it.” For many who play, the draft is the most anticipated, exciting and enjoyable part of the fantasy football season, but it’s far from a perfect process…Fantasy Football7 min readFantasy Football7 min read
