Industrial insight and articles from Winder.AI, focusing on the topic Case Study
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Wed Sep 13, 2023, by Winder.AI, in AI Product Development, Case Study, Generative AI, Software Development
We’re pleased to announce the release of Stable Audio, a new generative AI music service. Stable Audio is a collaboration between Stability.AI and Winder.AI that leverages state-of-the-art audio diffusion models to generate high-quality music from a text prompt.
Tue Jul 4, 2023, by Winder.AI, in Case Study, MLOps
Tractable.AI is a leading insure-tech company based in the UK and has made significant strides in the motor vehicle insurance sector by leveraging AI technologies. Their innovative approach has allowed them to automate various aspects of the insurance lifecycle, including the complex process of loss adjustment. This AI-driven strategy has not only increased their operational efficiency but also enhanced their service delivery, making them a preferred choice for many customers.
Tue Jul 4, 2023, by Winder.AI, in Case Study, Reinforcement Learning
Our financial client are based in the UK. They specialize in providing services to the finance industry. Their data science team embarked on a project to leverage reinforcement learning within their product offering. Winder.AI, world-leading authors and experts on reinforcement learning, helped them deliver their POC into production. Read on to find out more.
Mon Jun 19, 2023, by Phil Winder, in Case Study, AI Product Development, Talk, Software Engineering
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.
Thu Jun 15, 2023, by Winder.AI, in Case Study, MLOps
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.
Thu Apr 20, 2023, by Winder.AI, in Case Study, Reinforcement Learning
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.
Tue Mar 21, 2023, by Phil Winder, in MLOps, Case Study, Talk
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.
Mon Jan 30, 2023, by Winder.AI, in MLOps, Case Study
The UK’s communications regulator, Ofcom, commissioned Winder.AI to produce a report to improve their understanding of the end-to-end processes that support the creation and deployment of automated content classifiers used in moderating online content.
Tue Jan 24, 2023, by Enrico Rotundo, in MLOps, Software Engineering, Talk, Case Study
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.
Tue Aug 23, 2022, by Enrico Rotundo, in Case Study, Mlops
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.
