MLOps 101 -- What, why and how to get started today

The rise of artificial intelligence has become omnipresent in recent years, state-of-the-art models are open-sourced on a daily basis and companies are fighting for the best data scientists and machine learning engineers, all with one goal in mind: creating tremendous value by leveraging the power of AI. Sounds great, but reality is harsh as generally only a small percentage of models make it to production and stay there. In this blog post, we'll explain how companies can unlock the business value of AI by adopting MLOps practices. Based on years of experience in applying machine learning at ML6, we share a set of best practices that have proven to work for us when it comes to MLOps. When it comes to a definition for MLOps, we believe Google's definition is spot on: "MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops)."