AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

Machine Learning Presentation: Provenance and Lineage for Data, Pipelines, and Deployments

Published
Author
Dr. Phil Winder
CEO

Dr. Phil Winder shares experiences of Winder.AI’s machine learning consulting experience at a variety of large and small organizations.

Abstract

In this talk he focuses on how provenance and lineage, typically thought of as a model deployment problem, can help make the development of machine learning models more repeatable, understandable, and robust.

Discover the difference between lineage and provenance. Learn how to determine the “strength” of your lineage and how robust it is to failure.

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Databricks vs Pachyderm - A Data Engineering Comparison

Published
Author
Enrico Rotundo
Associate Data Scientist

Winder.AI has conducted a study comparing the differences between Pachyderm and Databricks. Both vendors are prominent in the data and machine learning (ML) industries. But they offer different products targeting different use cases. Modern, production-ready requirements present major challenges where data is evolving, unstructured, and big. This white paper investigates the strengths and weaknesses in their respective propositions and how they deal with these challenges.

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Reinforcement Learning Presentation: Cyber Security

Published
Author
Dr. Phil Winder
CEO

Dr. Phil Winder shares experiences of Winder.AI’s reinforcement learning consulting experience at a variety of large and small organizations.

Abstract

In this talk he focuses on RL applications, looking at the use of RL in cyber security and discusses one interesting case study about how Winder.AI helped an internal security team develop a tool to hack web application firewalls.

About This Series

Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.AI, Author of O’Reilly’s Reinforcement Learning and one of the founders of the MLOps Community, covering a range of topics about the use of machine learning operations (MLOps), reinforcement learning (RL), and machine learning (ML) in industry today.

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MLOps Presentation: How to Build Resilient AI With GitOps

Published
Author
Dr. Phil Winder
CEO

Dr. Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations.

Abstract

In this talk he focuses on how GitOps is a key ingredient in any ML platform to enhance resiliency and observability. Learn why it is important, what it involves, and how to implement it in this short 30 minute video.

About This Series

Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.AI, Author of O’Reilly’s Reinforcement Learning and one of the founders of the MLOps Community, covering a range of topics about the use of machine learning operations (MLOps), reinforcement learning (RL), and machine learning (ML) in industry today.

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

Published
Author
Dr. Phil Winder
CEO

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.

In each case we start in a data science notebook.

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Using Reinforcement Learning to Attack Web Application Firewalls

Published
Author
Dr. Phil Winder
CEO

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.

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Helping Modzy Build an ML Platform

Published
Author
Dr. Phil Winder
CEO

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

How To Build a Robust ML Workflow With Pachyderm and Seldon

Published
Author
Enrico Rotundo
Associate Data Scientist

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. Throughout the demo, I showcase how Pachyderm comes in handy with data lineage and automation in a critical scenario.

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How We Built an MLOps Platform Into Grafana

Published
Author
Dr. Phil Winder
CEO

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.

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Automating Cyber-Security with Reinforcement Learning

Published
Author
Dr. Phil Winder
CEO

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.

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