Artificial intelligence

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.

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

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.

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.

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

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.

Automate SOC audits with SAGE using agentic AI, QRadar, and watsonx Orchestrate for faster evidence collection, compliance, and reduced analyst workload.

In this article, we demonstrate using different prompts with different contexts to analyze how LLMs behave when asked generic-related questions. We show how the Granite 3.3-2b model is good at identifying associated quantifiers in generic phrases and the model's responses are context-driven too.

Open source project: TensorFlow

Repositories related to the scikit-learn Python machine learning library.