At Datmo, we spend a lot of time working to build a better product for customers deploying AI to production, and part of that is ensuring that we are offering the most seamless developer experience possible. While each customer has a unique set of constraints between their model type and production needs, there is one thing consistent for everyone – people want to spend less time on deployments, and more time working on their models and experiments.
- Building and deploying machine learning models for enterprise use cases can be time consuming, tricky, and even political within an organization. The technology is exciting — predictive intelligence and classification, for example, have great disruptive potential and enable organizations to build competitive advantages with their own data.
- Topics like machine learning, artificial intelligence and data science have been talked about at length over the last few years. But these topics have been around for ages — albeit with names that have changed over the years. The types of problems that developers solve in these fields all fit into what we call Quantitative Workflows — the process of starting with data and deriving insights, actions, and quantitative models.