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.

Data Management

IBM Solution: Data Fabric

Hybrid Cloud

CHEOPS 2021

ICML 2021

VLDB 2021

SMDS 2021

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 Operations

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 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.

Hybrid Cloud Platform

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

Data Management

Tools + code

Fybrik

Project CodeFlare

Datashim Framework

Xskipper

Publications

Data Readiness Report

Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs

Reimagining GNN Explanations with ideas from Tabular Data

Distributed In-memory Data Management for Workflow Executions

A unified storage layer for supporting distributed workflows in kubernetes

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

IBM Solution: Data Fabric

Related teams

AI for IT Operations

Hybrid Cloud Platform

Automated AI

Data and AI Security