From Experiments to Deployment : MLflow 101 | Part 01

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