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.
Related jobs
Jobs that call for the skills explored in this talk.
CARIAD
Berlin, Germany
Junior
Intermediate
Python
C++
+1
Wilken GmbH
Ulm, Germany
Senior
Amazon Web Services (AWS)
Kubernetes
+1
Matching moments
03:13 MIN
Taking WebAssembly beyond the browser with WASI
Using WebAssembly to run, extend, and secure your application
03:05 MIN
Running WebAssembly on the server with WASI
WebAssembly Revolution: Elevating JavaScript's Reach and Performance
03:12 MIN
Exploring the future of the WebAssembly ecosystem
WebAssembly Revolution: Elevating JavaScript's Reach and Performance
05:04 MIN
Demonstrating Wasm with Rust and the WASI interface
WebAssembly: The Next Frontier of Cloud Computing
02:17 MIN
Solving common developer challenges with WebAssembly
WebAssembly Revolution: Elevating JavaScript's Reach and Performance
01:50 MIN
Why WebAssembly is a good fit for cloud workloads
WebAssembly: The Next Frontier of Cloud Computing
01:12 MIN
Improving the developer experience beyond performance
The Future of Cloud is WebAssembly
02:58 MIN
Understanding WebAssembly and its initial industry adoption
Using WebAssembly to run, extend, and secure your application
Featured Partners
Related Videos
From ML to LLM: On-device AI in the Browser
Nico Martin
Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization
Andreas Christian
Machine Learning for Software Developers (and Knitters)
Kris Howard
Getting Started with Machine Learning
Alexandra Waldherr
Wasm Deep Dive - A Glance Behind the Scenes
Rainer Stropek
Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML
Linda Mohamed
What comes after ChatGPT? Vector Databases - the Simple and powerful future of ML?
Erik Bamberg
Develop AI-powered Applications with OpenAI Embeddings and Azure Search
Rainer Stropek
Related Articles
View all articles

.webp?w=240&auto=compress,format)

From learning to earning
Jobs that call for the skills explored in this talk.

WeCloudData
Remote
Python
Machine Learning
Continuous Integration



HMS Analytical Software GmbH
Ulm, Germany
Remote
Azure
Data analysis
Machine Learning
Amazon Web Services (AWS)


LinkiT
Amsterdam, Netherlands
Azure
DevOps
Python
PySpark
Terraform
+2


WeCloudData
Remote
CSS
GIT
HTML
REST
+7
