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.
- 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.
- 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.