Automated AI
We're building tools to help AI creators reduce the time they spend designing their models. Our goal is to allow non-experts across industries to build their own AI solutions, without writing complex code or performing tedious tuning and optimization.
Our work
How IBM is helping a major retailer stay ahead of the holiday crunch
Case studyGoal-oriented flow assist: supporting low code data flow automation with natural language
Technical noteSnap ML pushes AutoAI to deliver 4x-faster automated machine learning on IBM Cloud
ReleaseSimplifying data: IBM’s AutoAI automates time series forecasting
ReleaseResearchers can speed up their AI model training with Snap ML
ReleaseAI for AI set to make it easy to create machine learning algorithms
Research
Tools + code
Lale: a library for semi-automated data science
A Python library that makes it easy to automatically select algorithms and tune hyperparameters of pipelines that are compatible with scikit-learn, in a type-safe fashion.
View project →Snap ML
Snap ML is a library that helps data scientists in a Python stack accelerate the training and inference of popular ML models.
View project →AutoMLPipeline.jl
A package that makes it trivial to create and evaluate machine learning pipeline architectures, leveraging the built-in macro programming features of Julia.
View project →Lale.jl
A Julia wrapper of Python's Lale library for semi-automated data science.
View project →IBM Federated Learning - Community Edition
A Python framework for federated learning in an enterprise environment.
View project →DOFramework
A testing framework for decision-optimization (DO) model learning algorithms.
View project →
IBM Solution: AutoAI on IBM Watson Studio
Our recent work was developed into AutoAI in IBM Watson Studio. It enables data scientists to quickly build and train high-quality predictive models, and simplifies AI lifecycle management in a code-optional environment.
Publications
- 2023
- AAAI 2023
- 2023
- AAAI 2023
- 2023
- AAAI 2023
- 2023
- AAAI 2023
- 2023
- AAAI 2023
- 2023
- AAAI 2023
Tech Preview: IBM Federated Learning
Our research has been developed into a technology preview on the IBM Cloud Pak for Data. Federated Learning provides the tools for training an AI model collaboratively, by using a federated set of secure data sources.