ML System Development and Redundancy: Stop Rebuilding the Wheel
The article discusses the challenges of developing machine learning systems and the inefficiencies of maintaining helper functions. The author shares insights gained from creating a custom MLOps template that combines the benefits of templates and boilerplates. Key features of the template include observability, modularity, and improved developer experience.
- ▪The author emphasizes the importance of not reinventing the wheel in ML system development.
- ▪A custom MLOps template was created to enhance collaboration and scalability.
- ▪Key elements of the template include observability, schemas for data validation, and a unified engine for various tasks.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3948010) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Marwan Mohammed Posted on May 23 ML System Development and Redundancy: Stop Rebuilding the Wheel #machinelearning #softwareengineering #ai #tutorial Introduction For the longest time I’ve found myself asking one question repeatedly: “Do I really have to rewrite all of that every single time?” The answer for me at the time was to create a “helper functions” repository on GH, it was a painkiller that worked, until it didn’t.
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