Unlocking MLOps using Airflow: A Comprehensive Guide to ML System Orchestration
<p>This tutorial represents <strong>lesson 4 out of a 7-lesson course</strong> that will walk you step-by-step through how to <strong>design, implement, and deploy an ML system</strong> using <strong>MLOps good practices</strong>. During the course, you will build a production-ready model to forecast energy consumption levels for the next 24 hours across multiple consumer types from Denmark.</p>
<p><em>By the end of this course, you will understand all the fundamentals of designing, coding and deploying an ML system using a batch-serving architecture.</em></p>
<p>This course <em>targets mid/advanced machine learning engineers</em> who want to level up their skills by building their own end-to-end projects.</p>
<blockquote>
<p><em>Nowadays, certificates are everywhere. Building advanced end-to-end projects that you can later show off is the best way to get recognition as a professional engineer.</em></p>
</blockquote>
<h1>Table of Contents:</h1>
<ul>
<li>Course Introduction</li>
<li>Course Lessons</li>
<li>Data Source</li>
<li>Lesson 4: Private PyPi Server. Orchestrate Everything with Airflow.</li>
<li>Lesson 4: Code</li>
<li>Conclusion</li>
<li>References</li>
</ul>
<p><a href="https://towardsdatascience.com/unlocking-mlops-using-airflow-a-comprehensive-guide-to-ml-system-orchestration-880aa9be8cff">Visit Now</a> </p>