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.

Explain, Enhance and Enrich Your Data with Bacalhau Amplify

Published
Author
Dr. Phil Winder
CEO

Bacalhau is a project started under Protocol Labs, but has now spun out into Expanso, Inc. Expanso is a leading Web3 innovator specializing in developing next generation decentralized commodity services. This case study, which includes a video presentation, describes the proceeds of this collaboration. The Bacalhau team asked Winder.AI to help them develop a new AI product designed to perform data engineering at web-scale, backed by Web3 technologies.

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MLOps in Finance

Published
Author
Dr. Phil Winder
CEO

Our client is a UK-based financial services company specialising in offering loans for car finance. They leverage AI in their processes and are looking to expand its use. They realised that they would benefit from a comprehensive review of their machine learning operations from the perspective of MLOps experts, Winder.AI.

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Reinforcement Learning for Power Generation

Published
Author
Dr. Phil Winder
CEO

Genesis Energy is a power generation company in New Zealand that sells electricity generated by hydroelectric and hydrothermal generators to the domestic energy market. Currently, people control the decisions surrounding power generation and pricing. Genesis asked Winder.AI to help them develop a reinforcement learning-powered solution to automate generation and pricing. Reinforcement Learning Problem New Zealand has the enviable situation of possessing high-altitude lakes refilled with ice melt. Discharging the lake presents an ample kinetic energy store that can be utilised for power generation via a turbine.

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Presentation: MLOps and the Online Safety Bill

Published
Author
Dr. Phil Winder
CEO

This is a video of a presentation about the UK’s online safety bill. This places new burdens on social media companies to moderate content to keep the public safe. This video discusses how platforms are using MLOps to help operate AI solutions that allow them to scale and prevent hundreds of violating posts from being published every second.

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Do you like DAGs? Implementing a Graph Executor for Bacalhau

Published
Author
Enrico Rotundo
Associate Data Scientist

Winder.AI helped Protocol Labs, a technology company in the crypto space, to help develop Bacalhau, a novel decentralised computational platform that focuses on the AI lifecycle. This case study describes some of our work to develop this project but for more information view the Bacalhau website.

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RL Presentation: How to Overcome Reinforcement Learning Challenges

Published
Author
Dr. Phil Winder
CEO

This presentation discusses some of the greatest challenges for a reinforcement learning project. I will share some of the challenges we have faced and how we have overcome them. You’ll learn what to look out for and how that affects the planning process.

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RL Presentation: Finding and Executing Reinforcement Learning Projects

Published
Author
Dr. Phil Winder
CEO

Learn about the framework that we use to help organizations find and develop problems that are best suited to reinforcement learning. This includes information about how to find business problems that are suited to RL, how to derisk the development process, and what technical tasks are involved.

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An Introduction to Reinforcement Learning: All You Need to Know

Published
Author
Dr. Phil Winder
CEO

When a child wants to ride a bike, they learn by doing. This process of trial and error is also known as learning through reinforcement, because positive and negative experiences promote or discourage certain behaviours, respectively. Children learn to ride by avoiding actions that cause them to crash; for the thrill of feeling wind in their hair.

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Pachyderm ❤️ Spark ❤️ MLFlow - Scalable Machine Learning Provenance and Tracking

Published
Author
Enrico Rotundo
Associate Data Scientist

This article shows how you can employ three frameworks to orchestrate a machine learning pipeline composed of an Extract, Transform, and Load step (ETL), and an ML training stage with comprehensive tracking of parameters, results and artifacts such as trained models. Furthermore, it shows how Pachyderm’s lineage integrates with an MLflow’s tracking server to provide artifact provenance.

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