AI, Machine Learning, Reinforcement Learning, and MLOps Articles
Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.
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.
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.
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.
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.
This notebook builds upon the exponential bayesian model to implement simple backtesting. The idea here is to hold out data, train a model, and see how well the model is able to predict those results.
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.
Over the next couple of weeks I will be using Bayesian analysis to model the spread of COVID-19. Inspired by Alex Stage who started the Athena Project, I have committed Winder.AI to helping Athena reach its goals.
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.
Dr. Phil WinderCEO
Hajar KhizouAssociate Data Science Content Editor