MLOps - Winder.AI Blog

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

Helping Modzy Build an ML Platform

Helping Modzy Build an ML Platform

Aug 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

How To Build a Robust ML Workflow With Pachyderm and Seldon

Jul 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

How We Built an MLOps Platform Into Grafana

Jun 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.

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

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

Aug 2020, by hajar-khizou, in Data Science, MLOps

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.

Building an Enterprise NLP Platform

Building an Enterprise NLP Platform

Jun 2020, by Phil Winder, in Case Study, MLOps

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.

A Simple Docker-Based Workflow for Deploying a Machine Learning Model

A Simple Docker-Based Workflow for Deploying a Machine Learning Model

Apr 2020, by Phil Winder, in MLOps, Cloud Native

In software engineering, the famous quote by Phil Karlton, extended by Martin Fowler goes something like: “There are two hard things in computer science: cache invalidation, naming things, and off-by-one errors.” In data science, there’s one hard thing that towers over all other hard things: deployment.

DevOps and Data Science: DataDevOps?

Mar 2019, by Phil Winder, in Data Science, MLOps

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.