Winder.AI Blog

The Value of a Machine Learning Pipeline: Past, Present, and the Future of MLOps With Kubeflow

Mon Nov 1, 2021, by Phil Winder, in Machine Learning, MLOps

Industrial machine learning consulting projects come in a variety of forms. Sometimes clients ask for exploratory data analysis, to evaluate whether their data can be used to help solve a problem using artificial intelligence. Other times we use machine learning (ML) algorithms to automate decisions and improve efficiencies within a business or product. More recently we’ve refocused on reinforcement learning and customers ask us to help control some complex multi-step process.

Using Reinforcement Learning to Attack Web Application Firewalls

Fri Sep 3, 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

Wed Aug 25, 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

Tue Jul 27, 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

Fri Jun 11, 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

Wed May 5, 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

Mon Apr 5, 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

Wed Mar 17, 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

Mon Mar 1, 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

Thu Feb 18, 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.