Doug Ortiz<p>The ML Engineer and MLOps roles are merging. Companies are looking for a single person to build AND deploy models from start to finish. </p><p>This "full-stack" approach promises efficiency but at what cost? More pay, but also more pressure and risk of burnout.</p><p>What are your thoughts on this trend in the machine learning space? Is this the new standard?</p><p>Video: <a href="https://www.youtube.com/shorts/bOAND8zOkYE" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">youtube.com/shorts/bOAND8zOkYE</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a>, <a href="https://mastodon.social/tags/MLOps" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MLOps</span></a>, <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a>, <a href="https://mastodon.social/tags/TechCareers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TechCareers</span></a>, <a href="https://mastodon.social/tags/WorkLifeBalance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>WorkLifeBalance</span></a></p>