Data is the new oil. The problem is how to organize the refining process and operationalize the insights. Successful organizations build data science teams that incorporate different skill sets and responsibilities, instead of relying on a few elite individuals. The Data Science Factory is our vision for a CI/CD process for AI.
Empower data scientists, developers and analysts to build, run and manage AI models, and optimize decisions anywhere. Unite teams, automate AI lifecycles and speed time to value with real-time insights, risk scoring or next best offer initiatives.
Explainable AI is a set of processes and methods that allows human users to comprehend and trust the results and output created by AI algorithms, including its expected impact and potential biases.
Decision optimization streamlines the selection and deployment of optimization models, and enables the creation of dashboards to share results and enhance collaboration.
With easy-to-use IBM® SPSS®-inspired workflows, you can combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI platform.
Bring together open source frameworks.
Work with Jupyter notebooks, JupyterLab and CLIs — or in languages such as Python, R and Scala.
Unify tools and increase productivity for ModelOps
Operationalize enterprise AI across clouds. Govern and secure data science projects at scale.
Manage risks and regulatory compliance
Protect against exposure and regulatory penalties. Simplify AI model risk management through automated validation.
Choosing IBS Services for Analytics as partner for your Data projects brings you the following benefits:
- trusted advisor to build strategy with;
- early and visible return on investment;
- user requirements can be reviewed and revisited regularly;
- better control of the project from execution and delivery perspective;
- better financial control of the project.
Unlock your data for AI
Proud to have two teammates selected as IBM Champions
Technology is just an enabler. People are the ones who are getting things done.
A straightforward way to promote greater consistency in the development and application of data and information sources is to have common guidance that teams will adopt and then follow. This guidance, including both standard policies and guidelines, will need to be defined, supported, and evolved over time in a collaborative and open manner.
We are building Data Factories.
We are helping organisations to shift from the slow and bureaucratic strategies of traditional Data Management towards the collaborative, streamlined, and quality-driven agile/lean strategies that focus on enabling others rather than controlling them.
Data governance is essential to an organization’s overall strategy for data management and as part of a complete DataOps practice. Data governance practices provide a holistic approach to managing, improving and leveraging data to help you gain insight and build confidence in business decisions and operations while meeting regulatory requirements.
Modern Data Integration accelerates your projects through automated flow and pipeline creation across distributed data sources. A complete data integration solution delivers data from multiple on-premises and cloud sources to support a business-ready trusted data pipeline for DataOps.
Using the capabilities of the cloud-native architecture of IBM Cloud Pak for Data platform we deliver a full-featured Data and Analytics solution that combines key capabilities as hybrid data management, unified governance and integration, data science, industry model for Banking and analytics.