Industrial AI insight about machine learning, reinforcement learning, MLOps, and more...
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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.
Wed Dec 14, 2022, by Phil Winder, in Reinforcement Learning, Talk
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.
Tue Nov 22, 2022, by Phil Winder, in Reinforcement Learning, Talk
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.
Thu Oct 13, 2022, by Phil Winder, in Reinforcement Learning
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.
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.
Tue Aug 9, 2022, by Winder.AI, in Case Study, Reinforcement Learning
Winder.AI helped CMPC, a large paper milling company, to optimise their production process by using reinforcement learning. CMPC are now able to automate industrial processes that were previously manual. This case study describes our approach and the results.
Thu Jul 14, 2022, by Enrico Rotundo, Phil Winder, in Case Study, Mlops, Cloud-Native, Talk
Winder.AI worked with Grid.AI (now Lightning.ai) to investigate how Buildpacks can minimize the number of base containers required to run a modern platform. A summary of this work includes: Researching Buildpack best practices and adapting to modern machine learning workloads Reduce user burden and reduce maintenance costs by developing Buildpacks ready for production use Reporting and training on how Buildpacks can be leveraged in the future The video below presents this work.
Fri Jun 24, 2022, by Enrico Rotundo, in MLOps, Case Study
Winder.AI worked with Protocol.AI to evaluate general-purpose computation frameworks. A summary of this work includes:
