You're moving from research to production. How will you refine your feature engineering strategy?
Transitioning from a research setting to a production environment in machine learning (ML) requires a significant shift in your feature engineering strategy. In research, you may have the luxury of experimenting with complex features and models, but production demands efficiency, scalability, and robustness. As you move forward, refining your approach to feature engineering is critical to ensure that your ML models are not only accurate but also performant and maintainable in a real-world setting.
-
Andrejs S.Engineering Manager | 30+ Years in Tech | Top 1% LinkedIn Contributor on Machine Learning | The Quantified Self with ML…
-
Jack Blandin, PhDApplied ML | Building elite tech teams
-
Nebojsha Antic 🌟🌟 280x LinkedIn Top Voice | BI Developer - Kin + Carta | 🌐 Certified Google Professional Cloud Architect and Data…