Case Study

MLOps in Finance

Published

Jun 15, 2023

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. As a finance industry specialist, we understand the unique challenges and regulatory requirements in this sector.

Reinforcement Learning for Power Generation

Published

Apr 20, 2023

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. Hydroelectric power generation is therefore sustainable and low carbon.

Presentation: MLOps and the Online Safety Bill

Published

Mar 21, 2023

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.

How Social Media Platforms use MLOps and AI Governance to Help to Moderate Content

Published

Jan 30, 2023

Author

Dr. Phil Winder

CEO

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.

Do you like DAGs? Implementing a Graph Executor for Bacalhau

Published

Jan 24, 2023

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.

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

Published

Aug 23, 2022

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.

Optimising Industrial Processes with Reinforcement Learning

Published

Aug 9, 2022

Author

Dr. Phil Winder

CEO

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.

Buildpacks - The Ultimate Machine Learning Container

Published

Jul 14, 2022

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.

A Comparison of Computational Frameworks: Spark, Dask, Snowflake, more

Published

Jun 24, 2022

Author

Enrico Rotundo

Associate Data Scientist

Winder.AI worked with Protocol.AI to evaluate general-purpose computation frameworks as part of our MLOps consulting services. A summary of this work includes:

• Comprehensive presentation evaluating the workflows and performance of each tool
• A GitHub repository with benchmarks and sample applications
• Documentation and summary video for Bacalhau documentation website

Save 80% of Your Machine Learning Training Bill on Kubernetes

Published

Jun 6, 2022

Author

Dr. Phil Winder

CEO

Winder.AI worked with Grid.AI to stress test managed Kubernetes services with the aim of reducing training time and cost. A summary of this work includes:

• Stress testing the scaling performance of the big three managed Kubernetes services
• Reducing the cost of training a 1000-node model by 80%
• The finding that some cloud vendors are better (cheaper) than others

The Problem: How to Minimize the Time and Cost of Training Machine Learning Models

Artificial intelligence (AI) workloads are resource hogs. They are notorious for requiring expensive GPUs and large numbers of machines to train complex models on large datasets in a reasonable amount of time.