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.

MLOps Presentation: When do You Need an MLOps Platform Team?

Published
Author
Photo of Dr. Phil Winder
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 presents industry observations of MLOps team size and structure for a range of business sizes and domains. Learn more about how others structure their MLOps teams. Discover which problems you need to solve first.

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: Databricks vs. Pachyderm

Published
Author
Photo of Dr. Phil Winder
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 presents a white paper that discusses the differences between two leaders in the data engineering space – Databricks and Pachyderm. Learn how these two products differ, when to use each, and the pros and cons.

At the end of the talk Phil distils this information and presets best practices. There’s also some discussion of future trends and some ideas for aspiring open-source engineers.

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Machine Learning Presentation: Packaging Your Models

Published
Author
Photo of Dr. Phil Winder
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 packaging ML models for production serving. Learn about how the cloud vendors compare, what orchestration abstractions prefer, and how packaging tools seek to find the right abstractions.

At the end of the talk Phil distils this information and presets best practices. There’s also some discussion of future trends and some ideas for aspiring open-source engineers.

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GitOps for Machine Learning Projects

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Author
Photo of Dr. Phil Winder
Dr. Phil Winder
CEO

Not so long ago, developers used clunky consoles to provision infrastructure and applications. It wasn’t long before someone realized it was better to automate such a process via scripts and APIs. But it wasn’t until Hashicorp showed that APIs were not enough. Their insight was to declare a canonical representation of the infrastructure. You can then reconcile this declaration against the live view of the infrastructure.

In 2015-16 we helped WeaveWorks develop their cloud monitoring platform. We targeted Kubernetes but lamented the hackiness of our script-based deployment pipeline. The common pattern at that time was to run a script that ran kubectl apply over and over again.

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

Published
Author
Photo of Dr. Phil Winder
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
Photo of Enrico Rotundo
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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MLOps Presentation: How to Build Resilient AI With GitOps

Published
Author
Photo of Dr. Phil Winder
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

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Author
Photo of Dr. Phil Winder
Dr. Phil Winder
CEO

Industrial machine learning consulting and MLOps platform 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.

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Using Reinforcement Learning to Attack Web Application Firewalls

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Author
Photo of Dr. Phil Winder
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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