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  • Datmo partners with leading Indian fintech company Ezetap

    Palo Alto, CA – 7/10/2018 Datmo is pleased to announce the success of a strategic partnership with Ezetap, one of the largest Indian payments processing companies, powering millions of requests monthly. Ezetap is one of the many influential companies at the forefront of the Digital India movement, a government initiative to facilitate the empowerment of the Indian people through technology in an increasingly digital economy.
  • 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.
  • Deploying a machine learning model as an API with Datmo, Falcon, Gunicorn, and Python

    Learn how Datmo helps you wrangle the zoo of programming animals to create an easy-to-deploy machine learning API with an adaptable architecture. What’s an API? APIs (Application Program Interfaces) are software communication methods developed on a particular standard. Many companies have their own public API products that solve specific problems for developers —by passing their API a parameter as an input, the user can receive an output without needing to know (or understand) how the underlying task is done.
  • Learning Data Science? You may already know the basics

    There are countless people trying to get into data science lately, but many are intimidated by the idea of learning a new workflow. In just the last year alone, the number of people reading about and interacting with machine learning has jumped from ~100k in 2016 to over 10 million in 2017 worldwide.
  • 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.
  • 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.
  • 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.