AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Solving Three Common Manufacturing Problems with Reinforcement Learning

Published

Feb 18, 2021

Author

Dr. Phil Winder

CEO

Like many industries, manufacturing is experiencing an explosion in both the growth of and access to data. The data is complex and multi-faceted, for example the data may originate from the production line, the environment, through usage, or even from users. When viewed in this light, the explosion is often called “big data” and the effect called smart manufacturing (USA) or industrie 4.0 (Germany).

The data must be acted upon to be useful. Doing this manually, by humans, is often time-consuming and inefficient. Machine learning (ML) and reinforcement learning (RL) algorithms can automate decisions that are being made from the data. Our AI consulting and reinforcement learning development services help manufacturers implement these advanced solutions. These methodologies can be applied to deliver advanced manufacturing technologies, sustainable processes, and innovative products. These improvements are also implicitly linked to advancements in supply chain and inventory control technology, which I discuss in another article.

Inventory Control and Supply Chain Optimization with Reinforcement Learning

Published

Feb 7, 2021

Author

Dr. Phil Winder

CEO

Inventory control is the problem of attempting to optimize product or stock levels given the unique constraints and requirements of a business. It is an important problem because every goods-based business has to spend resources on maintaining stock levels so that they can deliver products that customers want. Every improvement to inventory control has a direct improvement the delivery of the business. Beginners study tactics, experts study logistics, so they say.

DataTalksClub - Industrial Applications of Reinforcement Learning

Published

Feb 7, 2021

Author

Reinforcement learning (RL), a sub-discipline of machine learning, has been gaining academic and media notoriety after hyped marketing “reveals” of agents playing various games. But these hide the fact that RL is immensely useful in many practical, industrial situations where hand-coding strategies or policies would be impractical or sub-optimal.

Following the theme of my new book (https://rl-book.com​), I present a rebuttal to the hyperbole by analysing five different industrial case studies from a variety of sectors.

GOTO Book Club: How to Leverage Reinforcement Learning

Published

Feb 4, 2021

Author

In this episode of GOTO’s book club I speak to Rebecca Nugent, Feinberg professor of statistics and data science at Carnegie Mellon univeristy. We talk, at length, about the application of reinforcment learning, specifically how it could be a way of creating truly personalised teaching curricula. It’s a really interesting discussion and it’s great to get someone of Rebecca’s calibre to bounce ideas off.

A Code-Driven Introduction to Reinforcement Learning

Published

Nov 11, 2020

Author

Notebook link

Abstract

Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, reinforcement learning has the potential to automate strategic-level thinking in industry.

In this presentation I present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it.

5 Productivity Tips for Data Scientists

Published

Aug 27, 2020

Author

Janet Miller

Associate Data Science Content Editor

Many articles talk about how professionals can make their workdays extra productive. However, for people like data scientists, whose jobs are extremely demanding, some tips are more valuable than others. For instance, it is important that you analyse how you spend your time. In the same breath, it would be in your best interest to organise your time into blocks, as these can help you focus on tasks – one at a time and without any interruption – and automate any process that you repeat. Of course, attaining a certain level of productivity requires more than just abiding by the aforementioned tips. That being the case, here are some other productivity tips you can follow and take inspiration from.

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

Published

Aug 26, 2020

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.

Improving Data Science Strategy at Neste

Published

Aug 7, 2020

Author

Dr. Phil Winder

CEO

Winder.AI helped Neste develop their data science strategy to nudge their data scientists to produce more secure, more robust, production ready products. The results of this work were:

• A unified company-wide data science strategy
• Simplified product development - “just follow the process”
• More robust, more secure products
• Decreased to-market time

Our Client

Neste is an energy company that focuses on renewables. The efficiency and optimization savings that machine learning, artificial intelligence and data science can provide play a key role in their strategy.

How We Work With Cloud-Native Data Science: An Interview With Phil Winder

Published

Jun 29, 2020

Author

Dr. Phil Winder

CEO

Building an Enterprise NLP Platform

Published

Jun 25, 2020

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.

Our Client

Our client, a large enterprise in the oil and gas industry, has thousands of Data Scientists working on a range of problems. This company operates throughout the world and is investing heavily in artificial intelligence (AI) to improve the entire organisation.