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One of the biggest challenges in data science is deploying a machine learning (ml) application to production. This phase is often complicated by the fact that the code base is sensitive to the operating system it’s running on, and that there can be a number of other complex dependencies needed for a machine learning applicationContinue reading “Deploying a Containerized Streamlit Application in 13(ish) Steps”
Note: This blog post is for Udacity’s Data Scientist Nanodegree Program. Introduction There are two main challenges when running a subscription-based business: getting customers and retaining them. The goal of this project was to analyze customer churn data for an online Software as a Service (SaaS) company to understand how attributes about the customer’s enrollmentContinue reading “Understanding Customer Churn”
Introduction There is a natural progression of an early career data professional’s abilities. They typically begin with learning basic data wrangling/munging skills including SQL and eventually progress to building machine learning models using APIs like scikit-learn. Once the foundational skills are in place, more advanced programming paradigms are used including object-oriented programming, custom modules, versionContinue reading “How I Passed the AWS Certified Cloud Practitioner Exam”
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