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
It has been almost an entire year ago since I have visited the Bonsai platform that I covered in my previous post. Since then a lot changed on the platform, so the time has come to revisit it and see in more detail what exactly changed (on the surface of course)!
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
Deepmind has achieved a huge milestone by publishing its latest paper around Reinforcement Learning in Nature - 23/DEC/2020. How they were able to train a Reinforcement Learning algorithm that masters Go, Chess, Shogi and Atari without needing to be told the rules.
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
In a previous article I explained how we can set-up an Nginx Kubernetes Ingress Controller, but how can we now monitor this? This is what I would like to tackle in this article, on how we are able to utilize Prometheus and Grafana to start visualizing what is happening on our Ingress Controller.
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?
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?
Facebook ReAgent - An End-to-End Use Case
Facebook decided to release their end-to-end applied reinforcement learning platform called ReAgent, after reading their vision on this, I have to say that I am completely hooked! They are providing an excellent view of Reinforcement Learning and the future adoption of it. But why is this and how can we get started with it?
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.
Writing a C# SDK for the OpenAI Gym using .NET Core
When we take a look at the OpenAI Gym on Github (https://github.com/openai/gym-http-api), we see that it does not have bindings available for C#. Now since I am a firm believer of .NET Core and what it brings to developer ecosystem, I decided to write one myself (https://github.com/Xaviergeerinck/dotnetcore-sdk-openai). Using what I learned in my previous blog post How to write a SDK in dotnet Core I created one that looks like this for the main method:
OpenAI Gym Problems - Solving the CartPole Gym
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)
So as we learned in the intro to Machine Learning, Reinforcement Learning is this technique where we have an agent who will take specific actions on an environment to try to reach an optimal state. But how can we illustrate this? Take a look at the following picture.
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.