This presentation will discuss in what circumstances bad data can affect your project along with some high-profile case studies. We will then spend as much time as we have going through some of the techniques you will need to fix that bad data. This is aimed toward those with intermediate-level Data Science experience.
Dr. Phil Winder
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1 min
Learn about how to implement the fastest time-series filters in Python.
Dr. Phil Winder
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7 minA comparison of Reinforcement Learning frameworks focusing on modularity, ease of use, flexibility and maturity by Phil Winder
Dr. Phil Winder
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37 min
Announcement: I have agreed to write a new book for O'Reilly on Reinforcement Learning. Read more.
Dr. Phil Winder
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4 min
Google has announced a new range of AI products to help Engineers rapidly develop and deploy models. Winder.AI helped develop content for the AI-Hub.
Dr. Phil Winder
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3 minDataDevOps or often just DataOps, is the combination of DevOps and Data Science. The goal is to empower Data Scientists to build reliable software. The downside, you need to be an expert at everything! Read more.
Dr. Phil Winder
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8 min
Instructions how to deploy a local version of Jenkins running on minikube. These instructions are for OSX, but it shouldn't be too hard to port to an another OS.
Dr. Phil Winder
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4 min
A little bit of research turned into a rant asking why is it so difficult to use two of the most popular Data Science libraries together? And a little bit about Scikit Pandas.
Dr. Phil Winder
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5 min
If you are running Kubernetes in a production environment, running Helm can be quite dangerous. Read this post to find out why.
Dr. Phil Winder
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3 min
Winder.AI developed novel attack detection algorithms for leading web application firewall company, Bitsensor. Using a combination of Data Science, Machine Learning and Cloud-Native technologies, we helped Bitsensor deliver the product their customers needed.
Dr. Phil Winder
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3 minCase studies and industry analysis from our team. No hype, roughly monthly.