Gaurav Chakravorty on building industrial grade machine learning systems for search, recommenders, and personalization systems.
Subscribe: Apple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.
Gaurav Chakravorty, is a Senior Manager at Discord, where he leads the team responsible for machine learning models for search and notification. Prior to discord Gaurav was a manager at Google where he led the team responsible for personalized podcast recommendations.
Gaurav Chakravorty:
There are two broad areas of applied machine learning within Discord: (1) anti abuse and safety, and (2) applied ML for discovery notification and discovery. Supporting both of these is the ML platform effort. Along with the ML effort is the golden data, the data engineering, data infrastructure, and certainly the data science initiatives. Now, within discovery and notifications, there are problems like, recommendations, ranking, semantic search, notifications, and app search. This also includes content summarization, which involves extracting multi-tenant knowledge graphs of interests.
Highlights in the video version:
- Introduction to Gaurav Chakravorty, Sr. Manager of ML at Discord
What is Discord?
Where does Discord sit in the platform universe?
What are potential applications of ML in Discord?
Is Discord decentralized? Can I setup a community?
Why should I consider job opportunities at Discord?
Projects and tools that someone would grow into
Challenges at Discord
ML practitioners and making the data golden
Information extraction, and quantitative finance
ML Community and risks associated with AI
ML roles and projects inside Discord
Experimentation
Low code, now code, and other industry trends
ML generic or specific to the Discord server
Regulations on embeddings in models
Discord server and where the model lives
Test confidential computing ideas and tools
Large language models
Graph neural networks beneficial to recommenders
How far along is Discord in graph neural networks
What are perks working for Discord?
Discord is making an impact on ML
Related content:
- A video version of this conversation is available on our YouTube channel.
- FREE Report: “2022 Trends Report: Data, Machine Learning, and AI”
- “What is Graph Intelligence?”
- Paolo Cremonesi and Maurizio Ferrari Dacrema: “Questioning the Efficacy of Neural Recommendation Systems”
- “Enterprise Applications of Reinforcement Learning: Recommenders and Simulation Modeling”
- Azeem Ahmed: “Data and Machine Learning Platforms at Shopify”
- Che Sharma: “Modern Experimentation Platforms”
- “Top Places to Work for Data Engineers”
Download the FREE report below:
[Image: Cartography from Pixabay]