
Buildpacks - The Ultimate Machine Learning Container
Buildpacks enable MLOps platform teams to reduce the maintenance burden of shared containers. Learn how Winder.AI helped Lightning AI develop Buildpacks for their service.
Enrico Rotundo
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14 min
Buildpacks enable MLOps platform teams to reduce the maintenance burden of shared containers. Learn how Winder.AI helped Lightning AI develop Buildpacks for their service.
Enrico Rotundo
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14 min
Learn more about the current state of the art general purpose computation platforms in this landscape analysis. This is a case study of a project completed by Winder.AI for Protocol Labs.
Enrico Rotundo
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5 min
Kubernetes' cluster auto-scaler allows for the scale out of large machine learning jobs. But which cloud you use, and what settings you provide, make a massive difference. Learn how we helped one client save 80% off their bill.
Dr. Phil Winder
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13 min
Dr. Phil Winder presents industry observations of MLOps team size and structure for a range of business sizes and domains.
Dr. Phil Winder
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1 min
This talk discusses presents our comparison of two leaders in the engineering space -- Databricks and Pachyderm.
Dr. Phil Winder
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1 min
This talk discusses common ways to package your machine learning models. Learn about best practice and the current state-of-the-art.
Dr. Phil Winder
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1 min
Learn how GitOps, the practice of being declarative, can help your artificial intelligence project development process become faster and more resilient.
Dr. Phil Winder
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13 min
Databricks vs. Pachyderm - two leading data engineering products. Find out how they compare in this white paper.
Enrico Rotundo
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18 min
Learn how GitOps for AI is a key ingredient in any ML platform to enhance resiliency and observability.
Dr. Phil Winder
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1 min
Kubeflow is an incredible project, and Kubeflow pipelines have become the defacto open-source pipelining solution for machine learning pipelines. But it's not good enough and we need to move past it. Learn why and how.
Dr. Phil Winder
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11 minCase studies and industry analysis from our team. No hype, roughly monthly.