We sat down for a chat with Angus, one of the machine learning engineers that worked to make this happen, to learn more about the work that went into shipping this technology. Hi Angus! Thanks for having a chat with us. First things first, how would you explain machine learning to a five-year old? Machine learning is the process of teaching computers to perform tasks that would usually only be possible for humans. We achieve this by feeding the computer with examples – lots and lots of examples – kind of like how humans learn stuff! Consider teaching a computer to label objects in photos. We would give it millions of photos containing cars, dogs, doctors, apricots, and so on. After learning patterns to classify between them, the computer gains the ability to “guess” which of them is in new photos. While that’s pretty neat, scientists have taken.
What does an average day for a machine learning engineer look like at GoodNotes
How an engineer at GoodNotes spends their time is unique to their project and their working style. I currently own two projects that span multiple teams. So 60% of my time is spent in meetings, writing plans across. Team members, reviewing proposals and code.Or giving feedback. On the other hand, an engineer who is currently focused. On a single module within a project spends more time writing code and technical plans. That being said, here’s Iceland Mobile Number List what an average day might. Look like in the company right now. In the morning, you plan your tasks for the day, check in on Slack for company- or team-wide updates, do some code reviews (or improve your own pull request based on the reviews received), or read a new ML paper. Time permitting, you get started with your main task of the day. Be it whipping out a technical proposal.
Tell us more about how you and the team shipped the handwriting recognition feature
Handwriting recognition engine at the end of 2022, which now supports 12 languages and powers millions of GoodNotes users. This was a highly complex, truly full-stack, and 3-year long project that occupied a full 10-person ML team. We needed help from external contractors to collect training data and continually assess the quality of our models. In-house, we staffed a Data Operations specialist to supervise the data verification process, 2 researchers to investigate data- and model-side techniques to improve the core algorithm, and at least 4 Email Data full-stack ML engineers and an additional iOS engineer responsible for deploying the models to our production environment and monitoring their performance. As with most other product launches, our in-house handwriting. A limited subset of users to collect feedback), to controlled experimentation for 10% of users, to eventually now powering 100% of users.