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  • Pushing the needle forward for AI deployments

    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.
  • Demystifying the ML, AI, and Data Science development ecosystem (Part 1: Build)

    This blog post is part 1 of a 3 part series explaining the landscape of what we call the quantitative oriented developer (QoD) pipeline; encompassing machine learning, artificial intelligence, and data science. There are already many tutorials for getting started with individual modeling frameworks or algorithms, but explanations outlining how all of the moving parts fit together in these broader workflows are lacking.
  • An Overview of Deploying Quantitative Workflows to Production

    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.
  • Collaboration in Quantitative Workflows

    The other day two of our engineers, we’ll call them Alex and Ignacio, were trying to improve the accuracy of a facial recognition model. The main goal: get the model to recognize Donald Trump in images, videos, and gifs with as high confidence as possible. The sub-goal: Alex wanted Ignacio’s help in improving the model for practical use cases, specifically when being used on a set of 10,000 new labelled images they received from a customer.
  • Quantitative Workflows: A New Paradigm for Everyone

    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.