In this post, we explore KServe, a model inference platform on Kubernetes designed for scalability. Building on our previous Kubeflow guide, we detail how to set up your first KServe endpoint, make predictions, and troubleshoot common issues. Follow our step-by-step instructions to seamlessly integrate KServe with your Kubeflow environment and enhance your machine learning deployment process.

In this post, I provide a comprehensive guide to setting up Kubeflow, a machine learning toolkit for Kubernetes. From initial preparation and downloading necessary binaries to installing all Kubeflow components and troubleshooting common issues, this step-by-step tutorial ensures a smooth installation process. You'll also learn how to create your first notebook and resolve potential errors, making it easier to leverage Kubeflow's powerful features for your machine learning projects.

Explore how to build advanced Retrieval-Augmented Generation (RAG) applications using MyScaleDB and LlamaIndex. This guide covers the installation of necessary tools, setting up a virtual environment, and creating an index for document categorization. Learn how to execute simple and filtered queries, and troubleshoot common issues. Enhance your understanding of integrating high-performance SQL vector databases with cutting-edge data frameworks for efficient LLM applications.

In this post, I document my journey of using QGIS, a free and open-source geographic information system, to plan gift deliveries. I outline the steps to install QGIS, add essential plugins, create a shapefile layer for mapping locations, and use ORS Tools for route planning. After configuring the map, I determined that my delivery route would take 3 hours. This process, while tailored to my use case, is versatile and applicable to various fields such as logistics, urban planning, and environmental management. QGIS provides robust tools for efficient spatial analysis and mapping tasks.

In this post, we delve into solving the Capacitated Vehicle Routing Problem (CVRP) by transitioning from traditional routing models to the advanced Mixed Integer Programming (MIP) approach. We'll start with the basics of creating a routing model using Google OR-Tools and then explore how to formulate and solve the CVRP using MIP for more optimized solutions. Whether you're new to vehicle routing or looking to enhance your optimization techniques, this comprehensive guide provides the insights and code examples you need.

This post demonstrates solving the Facility Location Problem (FLP) using OR-Tools and Micronaut. It covers defining the solver, variables, constraints, and objective function in Java. The implementation includes creating a Micronaut service and controller to handle file uploads, process the data, and compute the optimal solution. The example input file format and expected outputs are also illustrated, providing a complete guide to implementing and testing the FLP solution.

In this post, I explore solving a Traveling Salesman Problem (TSP) involving 200 cities using genetic algorithms within a Micronaut framework. Leveraging techniques like inversion, insertion, and swap mutations, I illustrate how to maintain genetic diversity and improve solution quality over generations. The implementation showcases significant performance improvements compared to previous solvers. This approach combines simulated annealing, genetic algorithms, and local search to tackle complex optimization challenges effectively.

Solve the Travelling Salesman Problem using Choco-solver and convert it into a powerful API with Micronaut. Explore the efficient solution and its integration for optimal city tours.

Explore setting up Graylog in your HomeLab for comprehensive log management. Configure MongoDB and OpenSearch, deploy Fluent Bit for log forwarding, and implement advanced features like Grok patterns and pipelines. Troubleshoot efficiently with tools like netshoot and tcpdump. Enhance your HomeLab environment with a scalable and efficient log management solution.