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

Wed Aug 26, 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

Thu Jun 25, 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

Fri Apr 24, 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?

Thu Mar 28, 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.