The latest articles on DEV Community by Dhruvil Karani (@dhruvilkarani). https://dev.to/dhruvilkarani https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F292967%2Fb9718397-bde8-4618-9baa-a765fdba5ca7.jpeg https://dev.to/dhruvilkarani en Dhruvil Karani Thu, 17 Mar 2022 06:57:27 +0000 https://dev.to/dhruvilkarani/software-engineers-have-this-advantage-in-machine-learning-over-everyone-else-17b8 https://dev.to/dhruvilkarani/software-engineers-have-this-advantage-in-machine-learning-over-everyone-else-17b8 <p><a href="https://res.cloudinary.com/practicaldev/image/fetch/s--EdDZ84oa--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ln9j3fgg3y819eh8f2wa.png" class="article-body-image-wrapper"><img src="https://res.cloudinary.com/practicaldev/image/fetch/s--EdDZ84oa--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ln9j3fgg3y819eh8f2wa.png" alt="" width="880" height="1091"></a></p> <p><em>Machine Learning is 80% software engineering.</em></p> <h2> How I distribute my project time </h2> <ol> <li><p>Write efficient queries to get data from multiple sources + data analysis - <em>30%</em></p></li> <li><p>Do ML - <em>10%</em> </p></li> <li><p>Model analysis - <em>10%</em></p></li> <li><p>Clean production-grade code + Package the application using Docker - <em>15%</em></p></li> <li><p>Create a whole pipeline on Airflow - <em>15%</em></p></li> <li><p>Deployment in a service using Golang - <em>20%</em></p></li> </ol> <p>If you are a software engineer, you are probably better than a data scientist in 1,3,4,5 and 6.</p> <h2> Some more good news </h2> <p>Tools like AutoML are automating a large part of step 2. Meaning there are two sustainable competencies people in ML can have</p> <ol> <li>Become really good at reasoning, experiment design and stakeholder management.</li> <li>Become really good at building infrastructure for ML. If you think that the ML part, which only has 10%, makes it insignificant, you feel the opposite. After all, you are creating software for the ML model. </li> </ol> <h2> Wondering what the catch is? </h2> <p>If you think that the ML part, which only has 10%, makes it insignificant, you feel the opposite. After all, you are creating software for the ML model. </p> <p>Hence, the ML model needs to work damn well. And you have only 10% of the time to do so, which means that you need to have sharp intuition, clear goals and vision on what could work and what won't.</p> <p>Consequently, it would help if you sharpened your sixth sense by reading books papers and catching up on conferences' latest research.</p> <p>I am new to DEV. This is my first post.</p> <p>Hi! I am Dhruvil; a chemical engineer turned ML engineer from India. I work with text and recommendations systems. If you liked what you just read, consider upvoting and sharing it. </p> machinelearning datascience python