How I Deployed a Machine Learning Model for the First Time
<h1><strong>Introduction</strong></h1>
<p>For as long as I’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 — a world of <strong><em>deployment </em></strong>and <strong><em>application</em></strong>.</p>
<p>Taking the leap from training a model to actually deploying it might seem intimidating. However, it’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 — at least for me — bringing my machine learning model to life, enabling it to interact and offer insights to users globally.</p>
<p>Let’s brace ourselves as we step outside the comfort of our Jupyter Notebooks, because we’re about to go on a deployment journey. Grab your coding cap, fasten your seatbelt, and let’s get ready for a thrilling ride into the world of machine learning deployment!</p>
<p><a href="https://medium.com/latinxinai/how-i-deployed-a-machine-learning-model-for-the-first-time-b82b9ea831e0"><strong>Learn More</strong></a></p>