Xavier Geerinck

Machine Teaching through Azure Project Bonsai - Series Overview

Autonomous Platforms are being deployed all around us, optimizing our daily processes and improving their efficiency and stability without the need of human intelligence. They learn by themself, iteratively improving themselves and staying ahead of the competition.

Azure Project Bonsai - Physical CartPole Project

In my previous post I revisited the Bonsai platform. Now it's time to actually start preparing to put it into production! (this will become more clearer over time 😉) To start off with a spoiler: Project Bonsai - It's impressive! 😎

The Azure Project Bonsai Platform Revisited

Gradually adopting Autonomous Systems in Production

Reinforcement Learning is something I have been covering a while now! Lately, I have seen this space growing tremendously but keep on seeing the same issue arising each time: "How can I as a company with an existing infrastructure / applications (= brownfield) adopt Reinfocement Learning?"

Analyzing ML/RL Model Performance with Azure Synapse Analytics Spark Pools

I have been following Azure Synapse Analytics since it was still in development and the mock-ups were not finished yet. The vision that Azure Synapse Analytics brings is simply amazing, bringing an End-to-End analytics platform that seamlessly mixes SQL Dedicated, SQL On-Demand, and Spark to prepare, analyze and publish your data needs.

Machine Learning Operations - MLOps explained

MLOps is the continuation of DevOps, extended for Machine Learning. It allows data scientists, data engineers, application developers, and the operations team to collaborate, reducing the time from model creation towards first production deployment.

Implementing Deepmind's MuZero Algorithm with Python

Auto scaling a HTTP triggered application in Kubernetes using Keda

Kubernetes is getting more popular everyday and it's no wonder why! When you are running applications on-premise or in-cloud, the possibility of having the applications in a portable way is a strong one! Removing the friction for scaling-out your application when you are ready for it, or even bursting scenarios.

Creating a simple "Hello World" docker application with Express and NodeJS

For a lot of infrastructure related blog posts I would like to utilize a very basic application that spins up a web server and allows us to access it. Once we access it we simple see a welcome message but with a delay that we can configure!

Monitoring the Kubernetes Nginx Ingress Controller with Prometheus and Grafana

Creating a Kubernetes Nginx Ingress Controller and create a rule to a sample application

Whenever you are creating an application that you want to expose to the outside world, it's always smart to control the flow towards the application behind it. That's why Kubernetes has something called Kubernetes Ingress. But what is it?

Unity ML-Agents - An Introduction

Reinforcement Learning with the Bonsai Platform

The Bonsai Machine Teaching platform has been released! Promising an easy to use environment for end-to-end Reinforcement Learning projects, starting with simulator selection / integration to algorithm configuration and training.

AI Battlecards - End to End Process for building and evaluating AI models

A couple of months ago it struck me that my personal knowledge in AI could be improved quite a bit. That's why I took the time to brush up on the different concepts ranging from data gathering until the evaluation of a deployed model.

Artificial Intelligence - How to measure performance - Accuracy, Precision, Recall, F1, ROC, RMSE, F-Test and R-Squared

We currently see a lot of AI algorithms being created, but how can we actually measure the performance of these models? What are the terms we should look at to detect this?

Web2Text - Deep Structured Boilerplate Removal - Running the Code

Facebook ReAgent - An End-to-End Use Case

Facebook's Open-Source Reinforcement Learning Platform - A Deep Dive

Facebook decided to open-source the platform that they created to solve end-to-end Reinforcement Learning problems at the scale they are working on. So of course I just had to try this ;) Let's go through this together on how they installed it and what you should do to get this working yourself.

Reinforcement Learning - Terminology

Writing a C# SDK for the OpenAI Gym using .NET Core

OpenAI Gym Problems - Solving the CartPole Gym

In a previous post we set-up the OpenAI Gym to interface with our Javascript environment. Let's now look at how we can use this interface to run the CartPole example and solve it with the theory that we learned in previous blog posts.

Bellman Equations

Summary

The Markov Property, Chain, Reward Process and Decision Process

As seen in the previous article, we now know the general concept of Reinforcement Learning. But how do we actually get towards solving our third challenge: "Temporal Credit Assignment"?

Installing OpenAI Gym in a Windows Environment

Reinforcement learning does not only requires a lot of knowledge about the subject to get started, it also requires a lot of tools to help you test your ideas. Since this process is quite lengthy and hard, OpenAI helped us with this. By creating something called the OpenAI Gym, they allow you to get started developing and comparing reinforcement learning algorithms in an easy to use way.

Multi-armed bandit framework

To start solving the problem of exploration, we are going to introduce the Multi-armed bandits framework. But what exactly does this solve? Just think that you are executing a clinical trial with 4 pills. You know that the pills have a survival rate but you don't know what that survival rate is. Your goal: find the pill with the highest chance of survival in X trials.

An introduction to Reinforcement Learning (RL)

An introduction to Machine Learning (ML)

Every few decennia there is a new cool kid around the block that makes an impact on the current world, an impact that is so big that we will have to adapt. Starting with the introduction of mainframe computing in 1950, the pc in 1975, the internet in 1980 and the introduction of mobile phones in 2007, towards the current age of Big Data which started in 2012.