How do you get your ML model into production?
It's easy to get mired in a mess of models, code, datasets, and metrics. Not to mention the infrastructure complexity of self-service Jupyter environments, clusters & pipelines. See the difference with Dotscience, a platform which manages the complexity for you, with easy deploys straight to Kuberenetes.
How can you tell when a model in production behaves unexpectedly?
Your production model might start giving the wrong answers—will you be able to tell? You want to know as soon as it happens, so you can make changes and address the problem. With Dotscience, you can statistically monitor models' behavior on unlabelled production data, by analyzing the statistical distribution of predictions.
As your development team grows, how do you keep track of your models?
Can you guarantee compliance with current and future regulation, or address stakeholders concerns with decisions made by a model? Can you keep track who created which model, and how? With Dotscience, you can forensically reproduce any issues and guarantee they are fixed, and reduce financial and reputational risks from AI. You can also easily share and collaborate on models.