I want to share my experience in the cloud, AI, and agents development areas.
This post shows how to use Langflow with watsonx.ai and a custom component for a “Temperature Service” that fetches and ranks live city temperatures. It covers installation, flow setup, agent prompting, tool integration, and interactive testing. Langflow’s visual design, MCP support, and extensibility offer rapid prototyping; future focus includes DevOps and version control.
The blog post outlines the process of setting up the Agent Developer Kit (ADK) to build and run AI agents locally using WatsonX Orchestrate Developer Edition. It involves setting up prerequisites, installing the necessary software, and loading an example agent—optional integration with Langfuse for observability.
The post explores the integration of Large Language Models (LLMs) in applications, stressing the need for effective production testing. It introduces the AI Operational Complexity Cube concept, emphasizing new testing dimensions for LLMs, including prompt testing and user engagement. A structured testing approach is proposed to ensure reliability and robustness.
This blog post explains how to deploy a fine-tuned model to IBM watsonx on IBM Cloud. It highlights the advantages of using this platform, such as avoiding infrastructure management and ensuring enterprise security, as well as detailed steps for configuration, deployment, and accessing the model from IBM watsonx.
The blog post outlines updates on fine-tuning a model with the InstructLab , detailing tasks like data preparation, validation, synthetic data generation, model training, and testing. It emphasizes the need for extensive and accurate input for effective training, while only minimal changes in the overall process since previous versions, particularly in handling data quality. This blog post contains updates related to my blog post InstructLab and Taxonomy tree: LLM Foundation Model Fine-tuning Guide | Musician Example.
This blog post explains the implementation of a custom travel assistant agent using the Bee Agent Framework. It covers creating a tool to suggest vacation locations and utilizing weather data, integrating with MLFlow for observability. The article emphasizes practical execution steps, system requirements, and the motivation behind combining location and weather insights for user queries.
IBM has launched the watsonx Developer Hub, consisting of four sections: Get Started, Capabilities, Guides, and Support. This Hub is a valuable resource for developers looking to learn about watsonx, emphasizing its significance in the development process.
This blog post explores the Bee Agent Framework integration with watsonx.ai, detailing the setup process for a weather agent example on MacOS. It discusses necessary installations, environment variable configurations, and code updates needed due to framework changes. The execution output illustrates how the agent retrieves current weather data for Las Vegas.
This blog post examines the Confusion Matrix as a metric for evaluating the performance of large language models (LLMs) in classification tasks, especially legal document analysis. It discusses the calculation of key classification metrics like Accuracy, Precision, Recall, and F1 score, emphasizing the challenges of using a broadly defined Ground Truth.
This blog post demonstrates how to simply add a dependency graph to a runnable chain for a LangChain AI Agent example with WatsonxLLM for a Weather Queries application.
Thomas Suedbroecker's Blog 