Last Friday I attended a keynote at the AI NextCon conference presented by Romer Rosales, Director of AI at LinkedIn. His presentation focused on how LinkedIn is using AI to drive their vision to ensure that “every professional interaction with LinkedIn is personal, relevant, and helps to provide opportunity”.Rosales explained that AI is behind almost everything a user sees or interacts with on LinkedIn, from a member’s feed to job recommendations to onsite search. By learning from a user’s behavior and engagement on LinkedIn, AI is used to “deliver the right information to the right user at the right time through the right channel”.LinkedIn also uses AI for internal purposes such as anomaly detection. If any key metrics are out of range, the LinkedIn team is alerted so that adjustments can quickly be made.Rosales went on to discuss how the use of AI has been adapted over time within his organization. While in the past it may have been used to solve one particular problem with one specific metric - such as what content should be served to each user based on their behavioral information - his team learned that other metrics may suffer with this approach.Over time, his team realized that they could not just optimize for one metric; they needed to take multiple metrics into account and find a balanced approach. This led them to evolve from looking at AI at the transactional level to looking at it from an ecosystem level to improve the overall member experience. They developed the Air Traffic Controller platform to maximize interactions on and off LinkedIn.When it comes to building its AI platform, the LinkedIn team’s goal has been to make end-to-end machine learning easy, fast, robust and automatic. By implementing tools for machine learning development, deployment and maintenance, Rosales’ team has optimized its platform for efficiency so that it can continue to drive AI innovations to improve the user experience.For Machine Learning teams to focus on delivering innovative solutions, it is critical for them to have the bandwidth to focus on the building and tuning the algorithms that support your solutions. At Appen, our expertise lies in providing high-quality, human-annotated data to power machine learning at scale. Working with us to improve your training data will allow your team to focus on product development and deliver greater value to your customers. Contact us today to learn more.
