Winder.AI Blog

Industrial AI insight about machine learning, reinforcement learning, MLOps, and more...

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Building an Enterprise NLP Platform

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.

Developing a Real-Life Project

Fri Jun 12, 2020, by Phil Winder, in Software Engineering

I’m often asked questions in the vain of “how did you figure that out?”. Other times, and I’m less of a fan of these, I get questions like “you estimated X, why did it take 2*X?”, which I respond with a definition of the word estimate. Both of these types of questions are about the research and development process. Non-developers, and especially non-engineers, are often never exposed to the process of research and development.

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

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.

COVID-19 Logistic Bayesian Model

COVID-19 Logistic Bayesian Model

Wed Apr 8, 2020, by phil-winder, in Data Science

This post builds upon the exponential model created in a previous post. The main issue was that there an exponential model does not include a limit. A logistic model introduces this limit. I also perform some very basic backtesting and future prediction.

COVID-19 Exponential Bayesian Model

COVID-19 Exponential Bayesian Model

Wed Apr 8, 2020, by phil-winder, in Data Science

The purposes of this notebook is to provide initial experience with the pymc3 library for the purpose of modeling and forecasting COVID-19 virus summary statistics. This model is very simple, and therefore not very accurate, but serves as a good introduction to the topic.

How to Start a Data Science Project With No or Little Data

How to Start a Data Science Project With No or Little Data

Wed Feb 26, 2020, by Hajar Khizou, in Data Science

Data is an essential asset of modern business. It empowers companies by surfacing unique insights about their customers and creates actionable products. The more data you possess, the better you meet and exceed your customers' expectations.

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it - NDC London

Thu Jan 30, 2020, in Data Science, Talk

Slides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies.