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.
- 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.
- Applications are now open for the first class of our new 6-week, projected oriented mentorship program. About the Opportunity The Datmo Applied AI/ML Fellowship is a project-based mentorship program designed to help aspiring AI/ML engineers hone their data modeling and technical communication skills through a project of their choosing, while receiving guidance and mentorship from industry experts.
- 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.
- 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.
- 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.