ml-assisted product
MLOps: Machine Learning Lifecycle
Of course, that is a gross over-simplification. As more models are being deployed in production, the importance of MLOps has naturally grown. There is an increasing focus on the seamless design and functioning of ML models within the overall product. Model Development can't be done in a silo given the consequences it may have on the product and business. We need an ML lifecycle that is attuned to the realities of ML-assisted products and MLOps.