What Is Machine Learning Operations (MLOps)?

Machine learning is a big deal. It's used to power everything from self-driving cars to personalized recommendations on Amazon (and if you're reading this, probably even the ad that just popped up). The potential applications are endless, and it's set to revolutionize our world in ways we can barely imagine but while machine learning is a vital tool in the right hands, it's also a pretty sensitive one. It requires much work to get right and even more work to maintain as time goes on. That's why there is MLOps—a new approach to controlling the entire lifecycle of a machine learning model—from its training to its retirement. With MLOps, you can rest easy knowing that your models are being cared for by experts who know how delicate they are. If you've ever felt that your computer is more intelligent than you are, don't worry—you're not alone. With the rise of artificial intelligence (AI) tools, we're seeing more instances where machines make decisions for us. We may not always be aware of these decisions, but they can greatly impact our lives. One of the most important goals of MLOps is to help stakeholders use AI tools to solve business problems while also ensuring an ML model's output meets best practices for responsible and trustworthy AI. Machine learning operations (MLOps) is a new term for a very old idea let's automate as much of the repetitive work as possible and make sure we can see what's happening at all times. This is a concept that has been introduced previously. It's just that it has just been applied to machine learning models. As you know, machine learning models are complex and challenging to manage in production—but that doesn't mean they can't be managed! With MLOps, data scientists still get to do what they love: develop machine learning models. But then they hand over those models to operations teams, who can keep tabs on how well the model is doing and make changes when necessary. The best part about MLOps? It improves communication between the two groups of people most likely to benefit from it: data scientists and operations teams!