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Nicolás Metallo

As seen in: Medium, Towards Data Science, KDnuggets
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Articles by Nicolás Metallo

Create beautiful and simple ML web apps that you can deploy at scale in a few steps

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By Nicolás Metallo, Aws Fargate, Aws Cdk
| Towards Data Science
This article will teach you how to build an ML-powered web app from scratch and deploy at scale to AWS Fargate from start to finish using Streamlit and AWS CDK. You can have a look at this repo for the code. We are going to create a web app that can do a magic trick for us. That trick will be to make certain things “disappear” from images.
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What is the state of the developer in 2020?

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By Joel Falconer Verified, Nicolás Metallo, Claudio Jolowicz, Amanda Treutler
| Medium
SlashData and Data Developer Economics collect data data to gain an insight into key trends for software developers in 2020: in this article we recap them. Table Of Contents Every year SlashData and Data Developer Economics surveys developers to gain an insight into key trends. They released their most recent State of the Developer last month which reached more than 17,000 2020 software developers in 159 countries. This research report delves into key developer trends for H1 2020 and beyond.
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Deploy your PyTorch model to Production

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By Nicolás Metallo, transforms.Center Crop
| KDnuggets Verified
By Nicolás Metallo, Audatex Following the last article about Training a Choripan Classifier with PyTorch and Google Colab, we will now talk about what are some steps that you can do if you want to deploy your recently trained model as an API. The discussion on how to do this with Fast.ai is currently ongoing (more) and will most likely continue until PyTorch releases their official 1.0 version.
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Deploy your PyTorch model to Production – Data Driven Investor – Medium

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By Nicolás Metallo
| Medium
Following the last article about Training a Choripan Classifier with PyTorch and Google Colab, we will now talk about what are some steps that you can do if you want to deploy your recently trained model as an API. The discussion on how to do this with Fast.ai is currently ongoing (more) and will most likely continue until PyTorch releases their official 1.0 version. You can find more information in the Fast.ai Forums, PyTorch Documentation/Forums, and their respective GitHub repositories.
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