IBM Architecture Patterns: GenAI, Data & Hybrid Cloud

Generative AI - capability model

Enterprise capabilities, essential for achieving strategic goals and operating requirements, are outlined in the Generative AI Architecture capability model.

The IBM AI platform

IBM's AI platform provides a comprehensive suite of tools that addresses the capabilities in the enterprise capability model.

AI Governance

AI governance is the ability to monitor and manage AI activities within an organization. It includes processes and procedures to trace and document the origin of data and models deployed within the enterprise; as well as the techniques used to train, validate, and monitor the continuing accuracy of models.

Securing Generative AI Solutions

Generative AI systems present a number of unique security challenges. Organizations must balance the power of generative AI with the risks that models will generate incorrect or undesirable outputs, disclose sensitive or private information, or execute unwanted actions.

The IBM maturity model for generative AI adoption

In 2020, IBM introduced the AI maturity framework for enterprise applications with 7 dimensions. With the advent of GenAI, we have aligned the IBM GenAI Architecture with an maturity model for GenAI Adoption.

Generative AI - Agentic AI

Agentic AI systems are able to autonomously plan and perform tasks on behalf of a user or another system. Agentic AI systems solve complex problems by breaking them down into series of smaller tasks and using available tools to interact with external systems, or perform computational tasks.

Generative AI - retrieval augmented generation

Large Language Models (LLMs) are often surprisingly knowledgeable about a wide range of topics but they are limited to only the data they were trained on. This means that clients looking to use LLMs with private or proprietary business information cannot use LLMs 'out of the box' to answer questions, generate correspondence, or the like.

Generative AI - conversation with agent assist

A multi-tiered support system in an organization can use a Large Language Model-powered conversational assistant, or chatbot, alongside human agents, offering efficient and comprehensive assistance to end-users.

Generative AI - generative search

Generative search is the combination of generative AI models with search capabilities; extending the search and retrieval capabilities of search solutions with the content generation and summarization capabilities of large language models (LLMs).

Generative AI - document summarization

Summarization is the ability to condense long documents into a concise summary that captures the key points of the larger work.

Generative AI - modernization and code generation

AI can revolutionize application development by generating, optimizing, and translating code across the entire software development lifecycle. The adoption of generative AI can lead to consistent software creation, optimal utilization of developer creativity, and enhanced developer skills.

Generative AI - code generation for Ansible

IBM watsonx Code Assistant (WCA) for Red Hat Ansible Lightspeed (RHAL) demystifies the process of Ansible playbook creation through generative AI-powered content recommendations.

Generative AI - code generation for Z

IBM watsonx Code Assistant (WCA) for Z is an AI-assisted mainframe application modernization solution that makes it easier for developers to incrementally modernize and refactor COBOL business services and selectively transform to high-quality Java code optimized for IBM Z.

Generative AI - AI augmented software development with assistants

One of the most exciting advancements in LLMs is the concept of Agents, also known as Assistants. These are specialized versions of Large Language Models (LLMs) that are pre-prompted and fine-tuned to support specific roles.

DevSecOps application lifecycle management

The DevSecOps deployable architecture creates a set of DevOps Toolchains and pipelines. DevSecOps uses Continuous Delivery (Git Repos and Issue Tracking, Tekton Pipelines, DevOps Insights, and Code Risk Analyzer), Secrets Manager, Key Protect, Cloud Object Storage, Container Registry and Vulnerability Advisor.

Red Hat OpenShift Container Platform on VPC landing zone

Red Hat OpenShift Container Platform on VPC landing zone is a deployable architecture solution that is based on the IBM Cloud for Financial Services reference architecture. It creates secure and compliant Red Hat OpenShift Container Platform workload clusters on a Virtual Private Cloud (VPC) network.

Hybrid Cloud Application Deployment with TechZone Deployer

IBM Technology Zone (TZ) has built a deployment automation system that allows IBMers, RedHat and Partner seller (sellers) teams to select from a wide range of IBM products and enables them to build unique, customized solutions.

VPC landing zone - Standard

The Standard variation of the VPC landing zone deployable architecture uses two Virtual Private Clouds (VPC), a Management VPC, and a Workload VPC to manage the environment and the deployed workload. Each VPC is a multi-zoned, multi-subnet implementation that keeps your workloads secure. A transit gateway connects the VPCs to each other and Virtual Private Endpoints are used connect to IBM Cloud services.

VSI on VPC landing zone - QuickStart

The QuickStart variation of the VSI on VPC landing zone deployable architecture creates a fully customizable Virtual Private Cloud (VPC) environment in a single region. The solution provides virtual servers in a secure VPC for your workloads. The QuickStart variation is designed to deploy quickly for demonstration and development.

VSI on VPC landing zone - Standard

The Standard variation of the VSI on VPC landing zone deployable architecture is based on the IBM Cloud for Financial Services reference architecture. The architecture creates a customizable and secure infrastructure, with virtual servers, to run your workloads with a Virtual Private Cloud (VPC) in multizone regions.