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.

Can you describe your use case?

We also faced the same problem with other tools and hence building UpTrain with customisation at the core of it. Would be interesting to see if your use case fits