The (Hidden?) Costs of Vertex AI Resource Pools: A Cautionary Tale
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 article details my experience with a sudden price increase in Vertex AI and the hidden culprit – a seemingly innocuous resource pool.Building a RAG for tabular data in Go with PostgreSQL & Gemini
In this article we explore how to combine a large language model (LLM) with a relational database to allow users to ask questions about their data in a natural way. It demonstrates a Retrieval-Augmented Generation (RAG) system built with Go that utilizes PostgreSQL and pgvector for data storage and retrieval. The provided code showcases the core functionalities. This is an overview of how the "chat with your data" feature of fitsleepinsights.app is being developed.Using Gemini in a Go application: limits and details
This article explores using Gemini within Go applications via Vertex AI. We'll delve into the limitations encountered, including the model's context window size and regional restrictions. We'll also explore various methods for feeding data to Gemini, highlighting the challenges faced due to these limitations. Finally, we'll briefly introduce RAG (Retrieval-Augmented Generation) as a potential solution, but leave its implementation details for future exploration.Custom model training & deployment on Google Cloud using Vertex AI in Go
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 control on the pipeline from the model definition to the hardware to use for training and deploying. At the end of the article, we will also see how to use the deployed model. All of this, in Go and with the help of Python and Docker for the custom training job definition.
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