Lifelong learning and MLOps are indeed complementary.
Lifelong learning (LL) can be defined as the set of learning algorithms and models that can deal with more and more data and/or tasks without forgetting (completely) the previous one and (usually) without fully retraining the model with all data that you have available now. So, in LL, we attempt to mimic the way humans continually learn (different tasks) throughout their lives and transfer knowledge/skills acquired in one task to other tasks (e.g. from the task of walking to the task of running) .
MLOps is the application of DevOps to the machine learning or data science context. In other words, it is more a software engineering practice, where you define a pipeline (or sequence) of tasks that need to be done (semi-)automatically until and after you deploy your software. So, MLOps is just DevOps, but, in addition to the common practices in DevOps, like continuous integration and continuous deployment, you also have ways to deal with the ML part of the software, like automatic retraining as more data is available, and automatic evaluation of the (new) trained model on a test dataset.
They are complementary, because MLOps is concerned with the automatization of the data science process (i.e. collection and cleansing of data, training of the model with the new data, evaluation, deployment of the new trained and evaluated model to a production server), while LL is (only/usually) concerned with the development of learning algorithms and models that can cope with new tasks and data (without being completely reconfigured or retrained), so it has nothing or little to do with continuous integration and continuous deployment. In MLOps, given that you may (automatically) retrain the model as more data is available, you may think that this is a form of lifelong learning, but you completely retrain the model and you may not be able to cope with different tasks, but you could indeed use LL techniques in MLOps.