Mlops

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

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 Video unsupported 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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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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Unit Testing Data: What is it and how do you do it?

Published
Author
Hajar Khizou
Associate Data Science Content Editor

Data Testing plays an indispensable role in data projects. When businesses fail to test their data, it becomes difficult to understand the error and where it occurred, which makes solving the problem even harder. If data testing is performed correctly, it will improve business decisions, minimize losses, and increase revenues. This article presents common questions about unit testing raw data. If your question isn’t listed, please contact us, and we will be happy to help.

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Building an Enterprise NLP Platform

Published
Author
Dr. Phil Winder
CEO

Winder.AI has built a state of the art natural language processing (NLP) platform for a large oil and gas enterprise. This work leveraged a range of cloud-native technologies and sophisticated deep learning-based (DL) machine learning (ML) techniques to deliver a range of applications. Key successes are: New NLP workflows developed in hours, not weeks. Hugely scalable, from zero to minimise cost to tens of thousands of concurrent connections. Enforced corporate governance and unification, without burdening the developer.

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DevOps and Data Science: DataDevOps?

Published
Author
Dr. Phil Winder
CEO

I’ve seen a few posts recently about the emergence of a new field that is kind of like DevOps, but not quite, because it involves too much data. Verbally, about two years ago and in blog form about a year ago, I used the word DataDevOps, because that’s what I did. I develop and operate Data Science platforms, products and services. But more recently I have read of the emergence of DataOps.

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