Akmal Chaudhri
Using WebAssembly for in-database Machine Learning
#1about 4 minutes
Introducing WebAssembly for in-database machine learning
WebAssembly enables running high-performance code like C++ or Rust directly inside a database, co-locating analytics with data.
#2about 3 minutes
Comparing methods for machine learning with databases
An overview of different approaches to database ML includes using Apache Spark, Python libraries, built-in vector functions, and OpenAI embeddings.
#3about 7 minutes
Why use WebAssembly for in-database analytics
Running Wasm UDFs inside the database avoids data movement, extends DBMS features, and executes sandboxed code at near-native speed.
#4about 4 minutes
Setting up the Wasm and Rust development environment
A step-by-step guide to installing the necessary one-time dependencies, including the Wasmtime SDK, Rust toolchain, and wasm-bindgen.
#5about 4 minutes
Building a Wasm sentiment analysis function in Rust
The process involves creating an interface definition file (.wit), managing dependencies with cargo, and writing Rust code to wrap a sentiment analysis library.
#6about 5 minutes
Compiling and deploying the Wasm UDF to the database
After compiling the Rust code into a Wasm module, a specific tool is used to upload and register the function within the database system.
#7about 6 minutes
Live demo of sentiment analysis on a movie dataset
A live demonstration shows how to execute the deployed Wasm UDF with a SQL query against a table of IMDB movie reviews.
#8about 26 minutes
Summary, resources, and audience Q&A
A recap of Wasm's benefits for database extensibility is followed by links to resources and an audience Q&A session on implementation and performance.
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