Orchestrating data science and machine learning in an industrial setting is difficult. The data landscape is challenging, littered with complex problems and without the necessary freedom to design experiments or create observational studies on real-world processes. On top of that, you must manage stakeholders, operate cloud technologies, write software or wrap your solution into a product required to run predictions 24/7/365 – all while supporting business operations! With all this on your plate, you will need strategies to successfully incorporate MLOps in your organisation. In this talk, we will explore: How to “bootstrap” ML Engineering (MLEng) and MLOps practices in your organisation. Different ways to organize your MLOps teams to create optimum value. Where to start on your MLOps journey. How to avoid some common “gotchas” of starting out in MLOps.