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.

Integrating third-party libraries as Unreal Engine plugins: solving the ABI compatibility issues on Linux when the source code is available

In this article, we will discuss the challenges and potential issues that may arise during the integration process of a third-party library when the source code is available. It will provide guidance on how to handle the compilation and linking of the third-party library, manage dependencies, and resolve compatibility issues. We'll realize a plugin for redis plus plus as a real use case scenario, and we'll see how tough can it be to correctly compile the library for Unreal Engine - we'll solve every problem step by step.

AutoML pipeline for tabular data on VertexAI in Go

In this article, we delve into the development and deployment of tabular models using VertexAI and AutoML with Go, showcasing the actual Go code and sharing insights gained through trial & error and extensive Google research to overcome documentation limitations.

Advent of Code 2022 in pure TensorFlow - Day 12

Solving problem 12 of the AoC 2022 in pure TensorFlow is a great exercise in graph theory and more specifically in using the Breadth-First Search (BFS) algorithm. This problem requires working with a grid of characters representing a graph, and the BFS algorithm allows us to traverse the graph in the most efficient way to solve the problem.