MLOps: Practical Lessons from Bridging the Gap Between ML Development and Production

After leading multiple machine learning teams through the transition from prototype to production, I've witnessed firsthand how the discipline of MLOps has evolved from a loosely defined concept to a critical enterprise function. This evolution hasn't been straightforward - many of the practices we now consider standard emerged through trial and error across the industry. In this article, I'll share insights from implementing MLOps across organizations of different sizes and maturity levels, highlighting current trends and offering practical guidance based on real-world implementation challenges.