Path to Productivity in Job Search and Job Recommendation AI at LinkedIn

A year ago, LinkedIn job search and recommendation AI teams were at the end of a growth cycle. They were fighting many fires at once: a high number of user complaints, engineers spending a significant amount of their time keeping our machine learning pipelines running, online infrastructure that wasn't supporting their growth, and challenges ramping new models to experiment. In this talk, Alex discusses how they all came together to manage these challenges and set themselves for their next phase of growth.

Take-aways

- Quality ML starts with good measurements and good data. Spend time thinking about your metrics and quality assurance process. - ML engineers don't produce code, they produce knowledge through experimentation. Good ML stacks and processes will be optimized to maximize the experimentation process. - Modeling decision needs to be taken in the context of the infrastructure and engineers that support them (and vice-versa). They must also be made assuming that our intention is to build an innovation process, not too much one good model to solve a problem. - Modeling decisions shouldn't be taken in a vacuum. We need to work with our infrastructure partners. - User complaints are good insights into how to improve our model. AI team involvement in treating them can go a long way. - ML is about continuous iterations and building knowledge. Modeling decisions should be made to optimize the modeling process, not just to get the next model out.

In this episode

Alex Patry

Senior Staff Software Engineer, LinkedIn

Alex has been a machine learning engineer at LinkedIn for almost seven years. He had tour of duties in LinkedIn Groups, content search and discovey, feed, and has been tech leading in LinkedIn Talent Solutions and Careers for the last two years. Prior to working at LinkedIn, Alex lived in Montreal where he completed a PhD in Statistical Machine Translation, then work for five years on information extraction.

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Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Skylar Payne

Host

Data is a superpower, and Skylar has been passionate about applying it to solve important problems across society. For several years, Skylar worked on large-scale, personalized search and recommendation at LinkedIn -- leading teams to make step-function improvements in our machine learning systems to help people find the best-fit role. Since then, he shifted my focus to applying machine learning to mental health care to ensure the best access and quality for all. To decompress from his workaholism, Skylar loves lifting weights, writing music, and hanging out at the beach!