Tag: machine-learning

How we deployed a scikit-learn model with Flask and Docker

In our last post we discussed our customer satisfaction prediction model. We used AzureML studio for our first deployment of this machine learning model, in order to serve real-time predictions. In this post we would like to share how and why we moved from AzureML to a Python deployment using Flask, Docker and Azure App Service. During this time we also tried Azure Function with Python. In addition, we open-sourced a sample Python API with Flask and Docker for machine…

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Predicting customer satisfaction

Measuring your users’ satisfaction with your product is very important ― it guides your product decisions, impacts your new features and teaches you a lot. At Soluto we recently started working on a new platform ― connecting people who need on-demand tech support of any kind to a “supporting user” (we call them “experts”), who can help them resolve any tech issue ―  think Uber for tech support. This platform fulfills two reciprocal user needs: one user uses our app to…

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Our journey to better personalization using a recommendation engine – part 2

We just introduced (in part 1) the different models we use to greater personalize the notifications and content we offer to our users. Now we’ll present our experiences when building our very first recommendation engine. Collaborative filtering – predicting whether a user will click on a notification When thinking about how to apply these recommendation methods in our use case, we identified two important factors – What will make a user click on a notification and open it Which content…

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Our journey to better personalization: using a recommendation engine – part 1

Imagine you just finished watching a great movie on Netflix, and immediately afterwards you receive the following recommendation for your next movie: If you’re into old Chinese movies, then you’re in luck. But if not, you’ll probably be frustrated and turned off by this terrible recommendation. This example shows how important the use of personalization is and how it impacts your users’ feelings and thoughts about your product. Recommendation engines are a very effective way to personalize your product and…

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