Carlos Costa

Raghu Ganti

Nick Fuller

Bishwaranjan Bhattacharjee

Mudhakar Srivatsa

Research

IBM’s CodeFlare significantly cuts the time to automate transfer learning tasks for foundation models 

We're creating a hybrid data fabric that provides secure, governed data access from anywhere, and minimizes total cost of ownership for AI and analytics.

Data Management

IBM Solution: Data Fabric

Hybrid Cloud

VLDB 2023

IC2E 2023

CIKM 2023

INFORMS 2023

Foundation Models

Hybrid Cloud Platform

Machine Learning

AI can help sift through the noise to identify significant patterns in the increasingly large pools of data generated by enterprises. We’re reimagining IT operations, using AI to automate core processes, better detection and diagnosis, preformance monitoring, and explainable action recommendations.

AI for IT

As organizations move to the hybrid cloud, they must protect sensitive data and comply with regulations that allow them to take advantage of AI. We’re designing systems to monitor and protect data, building trust in AI through robust evaluation, certification, and hardening against attacks.

Data and AI Security

Designing systems to monitor and protect sensitive data for robust and secure AI systems on the hybrid cloud.

The future of computing lies in the hybrid cloud. We're creating a hybrid data fabric that provides secure,  governed data access from anywhere, enables self-service discovery of the right data at the right time, and takes a holistic view at minimizing total cost of ownership for AI and analytics.

The hybrid cloud can help businesses tap into the potential of the cloud, while retaining the benefits of private environments. At IBM Research, we’re pursuing open-source standards and code to create an agile and scalable platform that can be deployed anywhere.

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.

Automated AI

A cloud native platform to unify data access, governance and orchestration, enabling business agility while securing enterprise data.

Fybrik

A kubernetes-based framework for hassle free handling of datasets.

Datashim Framework

A framework to simplify the integration, scaling and acceleration of complex multi-step analytics and machine learning pipelines on the cloud.



Project CodeFlare

A library for creating, managing and deploying data skipping indexes with Apache Spark

 Xskipper

CodeFlare drastically reduces time to set up, run, scale machine-learning experiments