Deploying machine learning models to production in order to perform inference, i.e. predict results on new data points, has proved to be a confusing and risky area of engineering.
Many projects fail to make the transition from the lab to production, partially because these difficulties are not addressed on time.
In my opinion, there are three major factors which make deployment challenging:
Google Health’s recent paper provides a fresh set of insights about what it takes to bring an AI solution to real clinical use. Below is a summary of some key takeaways which should be applicable to many companies and solutions in this space
for the last few years, Google Health has been making headlines over its AI based solution for detecting diabetic retinopathy (and diabetic macular edema) from retinal fundus images. The latest episode of their journey covers a post-development phase of clinical deployments, and a prospective study combined with a “human centered evaluation” of their solution.
Having worked on commercial AI-based solutions for healthcare (radiology and more) for a number of years, I find that these publications resonate deeply with my own experience and learning. …
Over the last few years, many software organisations have started developing products which leverage machine learning.
Like every new technology, ML involves a certain learning curve, and managing this learning curve is critical for a successful ML adoption.
Interestingly, many organisations focus so much on the areas they need to learn, that they miss the opportunities to leverage assets they already have, which are critical for their success.
A great example of this phenomenon is the “Data Science Unicorn” myth.
Some companies expect to hire a single data scientist, who will deliver their ML project practically single handedly.
This person is a “Data Science unicorn” — a mythical being who can magically execute a Data Science project on her…