Winder.AI Blog

Industrial AI insight about machine learning, reinforcement learning, MLOps, and more...

Using Reinforcement Learning to Attack Web Application Firewalls

Using Reinforcement Learning to Attack Web Application Firewalls

Sep 2021, by Phil Winder, in Reinforcement Learning, Case Study

Introduction Ideally, the best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems. One primary threat are payloads arriving from the public internet, with the attacker using them to discover and exploit vulnerabilities. For this reason, web application firewalls (WAF) are introduced to detect suspicious behaviour. These are often rules based and when they detect nefarious activities they significantly reduce the overall damage.

Helping Modzy Build an ML Platform

Helping Modzy Build an ML Platform

Aug 2021, by Phil Winder, in MLOps, Case Study

Winder.AI collaborated with the Modzy development team and MLOps Consulting to deliver a variety of solutions that make up the Modzy product, a ModelOps and MLOps platform. A summary of this work includes: Developing the Open Model Interface Open-sourcing chassis, the missing link that allows data scientists to build robust ML containers Model monitoring and observability product features MLOps and model management product features The Problem: How to Build An ML Platform Modzy’s goal is to help large organizations orchestrate and manage their machine learning (ML) models.

How To Build a Robust ML Workflow With Pachyderm and Seldon

How To Build a Robust ML Workflow With Pachyderm and Seldon

Jul 2021, by Enrico Rotundo, in MLOps, Case Study

This article outlines the technical design behind the Pachyderm-Seldon Deploy integration available on GitHub and is intended to highlight the salient features of the demo. For an in depth overview watch the accompanying video on YouTube. Introduction Pachyderm and Seldon run on top of Kubernetes, a scalable orchestration system; here I explain their installation process, then I use an example use case to illustrate how to operate a release, rollback, fix, re-release cycle in a live ML deployment.

How We Built an MLOps Platform Into Grafana

How We Built an MLOps Platform Into Grafana

Jun 2021, by Phil Winder, in MLOps, Case Study

Winder.AI collaborated with Grafana Labs to help them build a Machine Learning (ML) capability into Grafana Cloud. A summary of this work includes: Product consultancy and positioning - delivering the best product and experience Design and architecture of MLOps backend - highly scalable - capable of running training jobs for thousands of customers Tight integration with Grafana - low integration costs - easy product enablement Grafana’s Need - Machine Learning Consultancy and Development Grafana Cloud is a successful cloud-native monitoring solution developed by Grafana Labs.

Automating Cyber-Security with Reinforcement Learning

Automating Cyber-Security with Reinforcement Learning

May 2021, by Phil Winder, in Reinforcement Learning, Use Case

The best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems. The common suggestion is to test for security, often leveraging the expertise of security-focussed engineers or automated scripts. But there are two fundamental issues with this approach: 1) security engineers do not scale, and 2) scripts are unlikely to cover all security concerns to begin with, let alone deal with new threats or increased attack surfaces.

CloudNativeX Interview: Reinforcement Learning

CloudNativeX Interview: Reinforcement Learning

Apr 2021, in Reinforcement Learning, Talk

Join Lee Razo and Phil Winder for this comprehensive introduction to Reinforcement Learning, an area of machine learning in which problems are tackled with intelligent agents which take actions to maximize a specified reward. Phil (quite literally) wrote the book on this topic and he takes us through the fundamentals of RL, some common use cases as well as tips on how even a small or mid-sized company can get started with and benefit from RL.

The Future of Transportation Infrastructure: Reinforcement Learning

The Future of Transportation Infrastructure: Reinforcement Learning

Mar 2021, by Phil Winder, in Reinforcement Learning, Use Case

The lock-downs endured during the coronavirus pandemic have given many the opportunity to work from home, potentially for the first time. Along with the guilt of failing at home-schooling, trying to work with noisy babies or animals, the lock-down has entirely changed the way in which we travel. When I speak to people about the pandemic, the lack of commute is one of the few positives they can take away from this experience and has led some to even question why they are paying for accommodation in some of the most expensive areas in the UK.

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

Mar 2021, in Reinforcement Learning, Talk

InfoQ · Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning Charles Humble, friend and editor of InfoQ, was kind enough to ask me for an interview to talk more about my new book, in podcast format. From the blurb: In this episode of the InfoQ podcast Dr Phil Winder, CEO of Winder.AI, sits down with InfoQ podcast co-host Charles Humble. They discuss: the history of Reinforcement Learning (RL); the application of RL in fields such as robotics and content discovery; scaling RL models and running them in production; and ethical considerations for RL.

Solving Three Common Manufacturing Problems with Reinforcement Learning

Solving Three Common Manufacturing Problems with Reinforcement Learning

Feb 2021, by Phil Winder, in Reinforcement Learning, Use Case

Like many industries, manufacturing is experiencing an explosion in both the growth of and access to data. The data is complex and multi-faceted, for example the data may originate from the production line, the environment, through usage, or even from users. When viewed in this light, the explosion is often called “big data” and the effect called smart manufacturing (USA) or industrie 4.0 (Germany). The data must be acted upon to be useful.

Inventory Control and Supply Chain Optimization with Reinforcement Learning

Inventory Control and Supply Chain Optimization with Reinforcement Learning

Feb 2021, by Phil Winder, in Reinforcement Learning, Use Case

Inventory control is the problem of attempting to optimize product or stock levels given the unique constraints and requirements of a business. It is an important problem because every goods-based business has to spend resources on maintaining stock levels so that they can deliver products that customers want. Every improvement to inventory control has a direct improvement the delivery of the business. Beginners study tactics, experts study logistics, so they say.