As businesses increasingly rely on data-driven strategies, the integration of artificial intelligence (AI) into their operations has become imperative. However, many organizations still encounter challenges in implementing AI solutions effectively. Data often remains trapped in silos, or it is not adequately prepared for business use.
Moreover, organizations are grappling with outdated on-premises data infrastructures, which come with high maintenance costs and resource constraints. To address these issues, many are turning to Amazon Web Services (AWS) for its comprehensive suite of economical data storage services and superior technical support.
Recently, AWS and IBM announced the availability of IBM Cloud Pak for Data, a cohesive platform for data and AI, on the AWS Marketplace. This allows users to easily test, subscribe to, and deploy the Cloud Pak for Data solution on AWS.
Leveraging Red Hat OpenShift and integration with AWS services, this platform simplifies data access, automates data discovery and curation, and ensures the protection of sensitive information through automated policy enforcement for all users in an organization.
Key Applications of IBM Cloud Pak for Data
IBM Cloud Pak for Data includes several well-established applications such as:
- IBM Watson Knowledge for data governance
- IBM DataStage for data quality and integration
- IBM Watson Studio for model automation
- Tools for managing AI model risk
Use Cases Supported by IBM Cloud Pak for Data
This unified suite of data management, governance, AI, and machine learning (ML) products supports various use cases, including:
- Data access and availability: Break down data silos and streamline your data landscape for quicker, cost-effective value extraction.
- Data quality and governance: Implement governance solutions to ensure reliable business data.
- Data privacy and security: Effectively manage sensitive data within a comprehensive privacy framework.
- ModelOps: Automate the AI lifecycle and synchronize application and model pipelines for scalable AI deployments.
- AI governance: Enhance transparency and compliance in AI with improved visibility into model development.
Unlocking the Full Potential of Your Data
IBM Cloud Pak for Data, built on Red Hat’s OpenShift Container Platform, offers access to IBM Watson’s cutting-edge AI technology on AWS’s highly available, on-demand infrastructure. The platform’s auto-scaling capabilities allow for the rapid establishment of a unified data and AI platform at a global scale.
Users benefit from a streamlined data pipeline that utilizes existing AWS services to gather data and feed it directly into IBM Cloud Pak for Data, enabling real-time actionable insights. This capability allows for the creation of a federated data model and facilitates the extension of data and business services across multiple sources.
Prerequisites
A moderate familiarity with AWS services is necessary for this product. New to AWS? Check out the Getting Started with AWS and AWS Training and Certification pages for useful learning materials.
Familiarity with IBM Cloud Pak for Data components and services is also presumed. For those unfamiliar with IBM Cloud Pak for Data and Red Hat OpenShift, further resources are available.
It is highly recommended to consult the IBM Cloud Pak for Data Deployment Guide prior to utilizing this product.
Using AutoAI in Cloud Pak for Data
This article will introduce AutoAI as an example of how Cloud Pak for Data simplifies the model automation process. AutoAI, found within Watson Studio, automatically evaluates your data and produces customized candidate model pipelines tailored to your predictive modeling needs.
IBM Cloud Pak for Data is primed to connect to various AWS data sources, including:
- Amazon RDS for MySQL
- Amazon RDS for Oracle
- Amazon RDS for PostgreSQL
- Amazon Redshift
- Amazon S3
- Amazon Athena
For instance, if your data resides in Amazon Simple Storage Service (Amazon S3), you can connect S3 to IBM Cloud Pak for Data at the platform level, granting access to your S3 files for use by Watson services. Comprehensive instructions for establishing this connection are detailed in the IBM documentation.
To create the connection asset, you’ll need specific connection details, including:
- The bucket name containing the files.
- The endpoint URL, including the region code (e.g., https://s3.<region-code>.amazonaws.com).
- The AWS region.
- Credentials, which consist of an Access key and a Secret key.
Let’s choose a data source; in this scenario, we will utilize a market dataset to predict mortgage default risk. The following image illustrates the AutoAI configuration within a graphical interface.
Once the data file is parsed, options will appear to select one or more features or attributes from the dataset for production use. The experiment automates the process of exploring datasets, building features, and applying transformations.
The following image displays the tool as it navigates through candidate algorithms to create “pipelines” for data processing, ultimately generating models.
While this process may take some time based on the dataset’s size and complexity, the result is a selection of pipelines ordered by accuracy.
These models can be saved, deployed, and exposed as endpoints. Once an endpoint is created, users can access examples of how to interact with the model’s predictions in various programming languages.
Conclusion
IBM and AWS have provided a cohesive solution for acquiring and deploying IBM Cloud Pak for Data via Red Hat OpenShift. This integration helps organizations streamline and automate their data collection, organization, and analysis processes using AI and machine learning technologies.
Additionally, users of IBM Cloud Pak for Data can purchase and deploy optional software, known as cartridges, to their IBM Cloud Pak installation through the same selection and billing method. This allows customers to focus more on utilizing their systems rather than managing multiple payment and licensing environments.
To get started, download IBM Cloud Pak for Data on AWS Marketplace. For more insights on related topics, check out this blog post and learn about the authority on this subject at this link. If you’re looking for further guidance, this resource provides excellent information.
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