From Experiments to Deployment : MLflow 101 | Part 01
<h1>The Why</h1>
<p>Picture this: You’ve got a brand new business idea, and the data you need is right at your fingertips. You’re all pumped up to dive into creating that fantastic machine-learning model . But, let’s be real, this journey is no cakewalk! You’ll be experimenting like crazy, dealing with data preprocessing, picking algorithms, and tweaking hyperparameters till you’re dizzy. As the project gets trickier, it’s like trying to catch smoke — you lose track of all those wild experiments and brilliant ideas you had along the way. And trust me, remembering all that is harder than herding cats </p>
<p>But wait, there’s more! Once you’ve got that model, you gotta deploy it like a champ! And with ever-changing data and customer needs, you’ll be retraining your model more times than you change your socks! It’s like a never-ending roller coaster, and you need a rock-solid solution to keep it all together . Enter MLOps! It’s the secret sauce that brings order to the chaos </p>
<p>Alright, folks, now that we’ve got the <strong>Why</strong> behind us, let’s dive into the <strong>What</strong> and the juicy <strong>How</strong> in this blog.</p>
<p><a href="https://pub.towardsai.net/from-experiments-to-deployment-mlflow-101-40638d0e7f26"><strong>Website</strong></a></p>