Recommendation Systems Challenges at Twitter Scale
Who is this presentation for?ML Engineers, data scientists, engineering managers, software engineers
Twitter has amazing and unique content that is generated at an enormous velocity internationally in multiple languages. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Many traditional recommendation systems approaches dont work directly at the scale. This talk provides a window into the world of fast moving recommendations at massive scale and shares practical ideas on why this is such a fun problem to work on!
Prerequisite knowledgeFamiliarity with machine learning and software engineering. Some familiarity with NLP may be useful though not required. Finally, if there is some familiarity with recommendation systems like collaborative filtering, then it may be useful.
What you'll learn- How to balance scalability with complexity of algorithms - How to build a recsys with catalog of 100x Amazon's catalog
Ashish leads recommendation system teams for events and trends at Twitter. He focusses on building scalable ML & recommendation systems. Prior to that, he was a Senior Director of Data Science at Capital One. He used AI/ML to generate insights from vast amounts of data and build interesting B2B, B2C and Enterprise products. Previously, he co-founded GALE Partners and headed the Machine Learning group, building AI/ML based marketing automation products. He helped the company grow from 9 to 120 in 2 years and setup the India office. He has over 19 years of experience in the technology industry, an MBA from Kellogg School of Management and a B Tech from IIT BHU.
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