MLOps

Industrial insight and articles from Winder.AI, focusing on the topic MLOps

A Comparison of Machine Learning Model Monitoring Tools and Products

Published
Author
Dr. Phil Winder
CEO

Machine learning (ML) model monitoring is a crucial part of the MLOps lifecycle. It ensures that your models are performing as expected and that they are not degrading over time. There are many tools available to help you monitor your models, from open-source frameworks to proprietary SaaS solutions. In this article, I’ll compare some of the best open-source and proprietary machine learning model monitoring tools available today.

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MLOps in Supply Chain Management

Published
Author
Dr. Phil Winder
CEO

Interos, a leading supply chain management company, partnered with Winder.AI to enhance their machine learning operations (MLOps). Together, we developed advanced MLOps technologies, including a scalable annotation system, a model deployment suite, AI templates, and a monitoring suite. This collaboration, facilitated by open-source software and Kubernetes deployments, significantly improved Interos’ AI maturity and operational efficiency.

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MLOps in Insurance

Published
Author
Dr. Phil Winder
CEO

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.

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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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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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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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Buildpacks - The Ultimate Machine Learning Container

Published
Author
Enrico Rotundo
Associate Data Scientist

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.

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