How I Deployed a Machine Learning Model for the First Time

<h1><strong>Introduction</strong></h1> <p>For as long as I&rsquo;ve started with machine learning, Jupyter Notebooks have been my most loyal sidekick. From data preprocessing to model training, fine-tuning, and testing, Jupyter Notebooks have been there at every step to support me. However, I always knew that there is an entire world beyond these digital pages &mdash; a world of&nbsp;<strong><em>deployment&nbsp;</em></strong>and&nbsp;<strong><em>application</em></strong>.</p> <p>Taking the leap from training a model to actually deploying it might seem intimidating. However, it&rsquo;s a critical step that transforms a data science project from a theoretical experiment into a practical, real-world application. And I knew I had to take that extra step!</p> <p>In this article, we will embark on my journey of building a classification model for a Kaggle competition. We start from a typical EDA and pipeline building until reaching new-unexplored territory &mdash; at least for me &mdash; bringing my machine learning model to life, enabling it to interact and offer insights to users globally.</p> <p>Let&rsquo;s brace ourselves as we step outside the comfort of our Jupyter Notebooks, because we&rsquo;re about to go on a deployment journey. Grab your coding cap, fasten your seatbelt, and let&rsquo;s get ready for a thrilling ride into the world of machine learning deployment!</p> <h1>Playground Series Episode 5 Season 3: Ordinal Regression with a Tabular Wine Quality Dataset</h1> <p>Our journey starts with the fifth episode of Kaggle&rsquo;s Playground Series&rsquo; third season. This series, promoted by Kaggle, presents a variety of machine learning challenges, inviting users to boost their skills in data analysis, feature engineering, data cleansing, and machine learning pipeline construction.</p> <p><a href="https://medium.com/latinxinai/how-i-deployed-a-machine-learning-model-for-the-first-time-b82b9ea831e0"><strong>Website</strong></a></p>