Industrial AI insight about machine learning, reinforcement learning, MLOps, and more...
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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:
Mon Jun 6, 2022, by Phil Winder, in Cloud Native, MLOps, Case Study
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.
Wed May 11, 2022, by Phil Winder, in MLOps, Talk
Dr. Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations. Abstract In this talk he presents industry observations of MLOps team size and structure for a range of business sizes and domains. Learn more about how others structure their MLOps teams. Discover which problems you need to solve first. About This Series Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.
Wed Apr 6, 2022, by Phil Winder, in MLOps, Talk
Dr. Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations. Abstract In this talk he presents a white paper that discusses the differences between two leaders in the data engineering space – Databricks and Pachyderm. Learn how these two products differ, when to use each, and the pros and cons. At the end of the talk Phil distils this information and presets best practices.
