Building a RAG for tabular data in Go with PostgreSQL & Gemini

In recent months, I embarked on a journey to transform the UI/UX of a side project—bot.eofferte.eu, a SaaS platform that automates Amazon affiliate marketing on Telegram and streamlines the Amazon Associates onboarding process. The project’s architecture is straightforward: a Go backend powered by the labstack/echo framework, with UI rendering...

Starting from Unreal Engine 5.3, Epic Games added support for the so-called modern Xcode workflow. This workflow allows the Unreal Build Tool (UBT) to be more consistent with the standard Xcode app projects, and to be compliant with the Apple requirements for distributing applications… In theory! 😅 In practice this workflow is flawed: both the...

In the article Custom model training & deployment on Google Cloud using Vertex AI in Go we explored how to leverage Go to create a resource pool and train a machine learning model using Vertex AI’s allocated resources. While this approach offers flexibility, there’s a crucial aspect to consider: the cost implications of resource pools. This...

Gemini - the multimodal large language model developed by Google - is already available on Vertex AI for production-grade applications. As with any other Vertex AI product, it is possible to interact with it using clients built in different languages such as Python, Java, and Go or using plain HTTP requests. After all, Vertex AI is just a web...

This article shows a different approach to solving the same problem presented in the article AutoML pipeline for tabular data on VertexAI in Go. This time, instead of relying on AutoML we will define the model and the training job ourselves. This is a more advanced usage that allows the experienced machine learning practitioner to have full...