Tag Archives: MLProduction
Deploying Machine Learning Models in Production: Challenges, Strategies, and Best Practices
Deploying a machine learning (ML) model is one of the most exhilarating milestones in an ML project. However, as many seasoned practitioners have discovered, simply turning on a trained model does not guarantee success in the real world. In production, a model faces a host of challenges—from evolving data to complex software engineering requirements—that must…
Leveraging Knowledge Distillation for Embedded AI: A Comprehensive Guide
In today’s rapidly evolving AI landscape, deploying deep learning models on resource-constrained devices is both a challenge and a necessity. Embedded AI—where inference happens directly on devices like smartphones, IoT sensors, or embedded systems—demands models that are both efficient and effective. In this guide, we explore how to bridge the gap between high-capacity models and…
From Proof-of-Concept to Real-World Impact: How to Successfully Deploy Machine Learning Models
Machine learning (ML) has revolutionized industries by unlocking the potential to automate tasks, generate new insights from data, and create entirely new product experiences. Yet, many practitioners and aspiring ML engineers discover a significant gap between training a great model in a lab or a Jupyter Notebook and actually running that model in production to solve real…