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Artificial intelligence - IBM Developer
IBM Developer

Artificial intelligence

Next generation resources for AI builders to create trusted solutions

Artificial intelligence is the application of machine learning to build systems that mimic the problem-solving and decision-making capabilities of the human mind. It includes several disciplines such as machine learning, knowledge discovery, natural language processing, computer vision, and data science. Generative AI refers to deep-learning models that can generate novel, high-quality text, images, and other content based on the data with which they were trained.

29 January 2026

Article

From prompt engineering to fine‑tuning: Transforming document validation

Learn how fine‑tuning a small language model (Mistral 7B) and using a Granite 3.3 8B orchestrator solves the consistency challenges of large‑scale business document evaluation.

From prompt engineering to fine‑tuning: Transforming document validation

27 January 2026

Article

Model Context Protocol architecture patterns for multi-agent AI systems

Learn how Model Context Protocol enables scalable multi agent AI systems through client, server, and hybrid architecture patterns, LLM placement strategies, reusable AI agents, dynamic orchestration, enterprise design trade offs, and real world implementation guidance.

Model Context Protocol architecture patterns for multi-agent AI systems

26 January 2026

Blog post

Top 10 generative AI articles, tutorials, and learning paths in 2025

Discover the most popular content for 2025's hottest topic: generative AI

Top 10 generative AI articles, tutorials, and learning paths in 2025

22 January 2026

Blog post

Cassandra for AI: No more Small Data

A database that adapts and grows with your dataset in the age of AI

Cassandra for AI: No more Small Data

21 January 2026

Article

High-performance SnapML preprocessing for machine learning on IBM Z

Boost machine learning performance on IBM Z with SnapML preprocessing that uses scikit learn pipelines, optimized C++ execution, efficient data transformations, graph feature engineering, and consistent training to production workflows for accurate and low latency enterprise inference.

High-performance SnapML preprocessing for machine learning on IBM Z

10 December 2025

Tutorial

Deploy MCP Composer on IBM Cloud Code Engine using Docker

Learn to deploy MCP Composer on IBM Cloud Code Engine using Docker for scalable AI orchestration, FastAPI tool integration, and serverless container hosting.

Deploy MCP Composer on IBM Cloud Code Engine using Docker

29 October 2025

Tutorial

Build a local AI co-pilot using IBM Granite 4, Ollama, and Continue

In this tutorial, learn how to set up a local AI co-pilot in Visual Studio Code using IBM Granite 4, Ollama, and Continue, overcoming common enterprise challenges such as data privacy, licensing, and cost. The setup includes open-source LLMs, Ollama for model serving, and Continue for in-editor AI assistance.

Build a local AI co-pilot using IBM Granite 4, Ollama, and Continue

21 October 2025

Tutorial

Set up an end-to-end use case for local AI agents with MCP server and watsonx Orchestrate

Learn to integrate MCP with watsonx Orchestrate Developer Edition using Docker Compose to build, test, and monitor AI agents in a local environment.

Set up an end-to-end use case for local AI agents with MCP server and watsonx Orchestrate

08 October 2025

Series

The future of Java and AI

Learn how the Java ecosystem is adapting to the AI era and how IBM and Red Hat are investing to secure its future.

The future of Java and AI

08 October 2025

Article

Granite and watsonx: Open Models for the Next Generation of Enterprise AI with Java

Learn about using Granite models and watsonx for enterprise AI with Java. Understand the importance of openness, interoperability, and long-term support in AI adoption. Granite models provide an open foundation for AI, while watsonx delivers enterprise governance and lifecycle management. The combination of Quarkus, LangChain4j, Jakarta EE, and Red Hat OpenShift AI provides a complete story for confident AI adoption. This approach aligns AI adoption with the principles that made Java successful, ensuring investments remain valuable over time.

Granite and watsonx: Open Models for the Next Generation of Enterprise AI with Java

08 October 2025

Article

From Standards to Speed: Why Quarkus Leads the AI-Ready Jakarta EE Future

The future of enterprise Java and AI is secure with Quarkus and Jakarta EE. Quarkus provides a cloud-native Java stack with build-time optimization and instant startup. It supports AI-powered applications through LangChain4j. IBM invests in both, driving innovation and ensuring stability. Quarkus allows enterprises to innovate safely, designing AI-driven services that can be deployed in production and supported by Jakarta EE components. This ensures a stable and secure future for enterprise Java and AI.

From Standards to Speed: Why Quarkus Leads the AI-Ready Jakarta EE Future

08 October 2025

Article

AI at Scale: Red Hat OpenShift AI as the Enterprise Backbone for Java Workloads

Discover the challenges of running AI in production at enterprise scale. Java alone is not enough, as AI workloads require a platform designed for scale. Red Hat OpenShift AI provides the operational backbone, extending Kubernetes with capabilities for building, deploying, and managing AI workloads. It ensures governance, observability, and portability, aligning with open standards. This allows Java developers to treat AI workloads with the same discipline as other enterprise services, making AI viable at enterprise scale. IBM and Red Hat are investing in open standards and products to secure the future of enterprise Java and AI.

AI at Scale: Red Hat OpenShift AI as the Enterprise Backbone for Java Workloads
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Artificial intelligence - IBM Developer