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.

CloudNativeX Interview: Reinforcement Learning

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Join Lee Razo and Phil Winder for this comprehensive introduction to Reinforcement Learning, an area of machine learning in which problems are tackled with intelligent agents which take actions to maximize a specified reward.

Phil (quite literally) wrote the book on this topic and he takes us through the fundamentals of RL, some common use cases as well as tips on how even a small or mid-sized company can get started with and benefit from RL.

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The Future of Transportation Infrastructure: Reinforcement Learning

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Dr. Phil Winder
CEO

The lock-downs endured during the coronavirus pandemic have given many the opportunity to work from home, potentially for the first time. Along with the guilt of failing at home-schooling, trying to work with noisy babies or animals, the lock-down has entirely changed the way in which we travel.

When I speak to people about the pandemic, the lack of commute is one of the few positives they can take away from this experience and has led some to even question why they are paying for accommodation in some of the most expensive areas in the UK.

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Solving Three Common Manufacturing Problems with Reinforcement Learning

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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.

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Inventory Control and Supply Chain Optimization with Reinforcement Learning

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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.

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DataTalksClub - Industrial Applications of Reinforcement Learning

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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.

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GOTO Book Club: How to Leverage Reinforcement Learning

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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.

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A Code-Driven Introduction to Reinforcement Learning

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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.

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5 Productivity Tips for Data Scientists

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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.

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

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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.

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