MLOps

Industrial insight and articles from Winder.AI, focusing on the topic MLOps

Save 80% of Your Machine Learning Training Bill on Kubernetes

Published
Author
Dr. Phil Winder
CEO

Winder.AI worked with Grid.AI to stress test managed Kubernetes services with the aim of reducing training time and cost. A summary of this work includes: Stress testing the scaling performance of the big three managed Kubernetes services Reducing the cost of training a 1000-node model by 80% The finding that some cloud vendors are better (cheaper) than others The Problem: How to Minimize the Time and Cost of Training Machine Learning Models Artificial intelligence (AI) workloads are resource hogs.

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

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 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.

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MLOps Presentation: Databricks vs. Pachyderm

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 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.

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Machine Learning Presentation: Packaging Your Models

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 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

Published
Author
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.

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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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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.

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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.

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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 The Problem: How to Build An ML Platform Modzy’s goal is to help large organizations orchestrate and manage their machine learning (ML) models.

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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.

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