One of the fundamental activities during each stage of the ML model life cycle development is collaboration. Taking an ML model from its conception to deployment requires participation and interaction between different roles involved in constructing the model. In addition, the nature of ML model development involves experimentation, tracking of artifacts and metrics, model versions, etc., which demands an effective organization for the correct maintenance of the ML model life cycle. Fortunately, there are tools for developing and maintaining a model's life cycle, such as MLflow. In this article, we will break down MLflow, its main components, and its characteristics.

Not enthusiastic about buying an expensive GPU or optimizing cloud services bills? Wish there was a better way to do it? Luckily for you, the answer to the last question is yes. This precious GPU will train your deep learning models faster. This article is both for the Hands-on RL course students as well as for any deep learning developer out there looking for faster training loops.