• Setting up your Data Science and AI dev environment in 5 minutes

    Whether you’re a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. While containerization tools like Docker have truly revolutionized reproducibility in software, they haven’t quite caught on yet in the data science and AI communities, and for good reason!
  • 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.
  • 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.
  • 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.
  • 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.
  • Tracking and Reproducibility in Quantitative Workflows

    Documenting your work is necessary, but boring, regardless of the type of work you do. While tracking and reproducing work for most generic web-connected applications and workflows is becoming more standardized (i.e., document state-saving and tracking through Google Docs and code version control system like Github) there is currently no widely accepted standard or simple automation for data science and machine learning.
  • 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.