![]() The outer loop: After testing and evaluation, only the most promising models meeting the bar for the offline metrics that we believe align with business metrics move forward to a production deployment phase.In this loop we iterate to find the best version of the model. The middle loop: Only the promising models move past the model exploration phase to the refinement phase.Here an ML practitioner wants to write and run quick experiments offline and fail fast to succeed sooner. ![]() The inner loop: Typically we start with a business objective, frame it as an ML Problem, collect requisite data, conduct exploratory data analysis (EDA) to understand it, generate relevant features, try various ideas/hypotheses, explore models, evaluate the models, and iterate.Many of us have seen some variation of the ML Lifecycle as expressed in Figure 1, and can identify with the various stages and loops presented here: The machine learning (model) development lifecycle, as we ML practitioners (applied scientists, data scientists, machine learning engineers) know, is a cyclic iterative process.įigure 1: ML development lifecycle as inner, middle and outer loops The goal is to help our ML product teams iterate quickly through various stages of the machine learning development lifecycle, shipping continuously and successfully and bringing value to Zillow’s end users.īefore we talk about our solutions in this area, let’s understand the various challenges in the machine learning development lifecycle and why it is important to address them. Standardized platform solutions not only provide economies of scale for infrastructure but also an easy onboarding user experience that seamlessly integrates with various internal platforms like our data and experimentation platforms as well as monitoring and alerting subsystems. ![]() Many Zillow features are powered by data and machine learning (ML), ranging from providing the best home recommendations, to enabling textual home insights on listings, to generating floor plans, to enabling users to perform semantic search, and to optimizing connections with our Premier Agents ® partnerships - to list just a few.Īs machine learning is central to so many product scenarios, it has been critical for us to invest in platform solutions. This includes putting an estimate of a home’s market value - called the Zestimate ® - on every rooftop, giving people the power to make informed decisions about one of the most important transactions of their lifetime. ![]() Our mission is to give people the power to unlock life’s next chapter and help them find, make a winning offer on, and purchase their next home through low-friction digital solutions. Zillow was founded with a goal of “turning on the lights” for consumers, and it continues as a core value of the company today. ![]()
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