Generally, MLOps helps in reducing engineering headaches. During our user interviews and customer calls, we realized very early that customization is key for ML

Generally, MLOps helps in reducing engineering headaches. During our user interviews and customer calls, we realized very early that customization is key for ML model monitoring since all models are different. Thus, we have built the framework to lessen the engineering headache while allowing customizability (think PyTorch). Would love to know your thoughts on this.