Deploying your first ML model is a huge milestone, but it’s not as simple as just hitting “go.” Before you even get there, you need to ask yourself—do you really need ML, or would a rule-based system do the job just fine? One of the biggest headaches you’ll face is data; what works in training might completely fall apart in production because real-world data is messy and always changing. Deployment isn’t just about shipping a model—it’s about setting up the right pipelines, making sure there’s clear ownership, and avoiding disasters like outdated models or broken API contracts. And here’s the kicker: once your model is live, your job isn’t done—monitoring and continuous improvement are what keep it from becoming useless over time.