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.

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.

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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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Improving Data Science Strategy at Neste

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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.

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Building an Enterprise NLP Platform

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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.

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Developing a Real-Life Project

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Dr. Phil Winder
CEO

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.

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