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>Read More</strong></a></p>