Data Science in Production is the Podcast designed to help Data Scientists and Machine Learning Engineers get their models in to production faster. We focus on the tool, techniques and people of machine learning.
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Episode 6: The Global AI Bootcamp with Henk Boelman
26:29
26:29
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26:29
This Episode is recorded at the Intelligent Cloud Conference in Copenhagen. It features an interview with Henk Boelman Microsoft AI MVP and Cloud AI Architect for Heroes in the Netherlands. We discuss a lot of really interesting subjects. Kicking off with Henk's session at Intelligent Cloud - Where he is using DevOps to accelerate models in to prod…
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Episode 5: Data Lakes for Data Science
44:24
44:24
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44:24
In this episode we explore what is a data lake, and how to build a lake which enables data science teams to deliver models faster. We need somewhere we can store and access data which is indexed, searchable and always available. A good data lake will do this and more. Listen to find out how you can build a data lake to accelerate your teams machine…
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Episode 4: MLFlow with Matei Zaharia
31:48
31:48
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31:48
I caught up with the creator of Apache Spark and Databricks founder Matei Zaharia at this year's Big Data London. We discussed the release of MLFlow, Databricks and project Dawn.
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Episode 3: Version control for Data Science
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41:46
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41:46
In this episode I talk about why you should have all your data science projects in version control / source control. I discuss why it is important, how to get started, the gotchas and how to version control data science projects.
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Episode 2: Deploying Deep Learning models with TimTem
1:03:35
1:03:35
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1:03:35
In this episode, I am joined by #TimTem AKA Dr Tim Scarfe and Dr Tempest van Schaik. Tempest and Tim are Machine Learning Engineers for Microsoft. This podcasts focuses on a project they delivered for Confused.com. We discuss deploying Deep learning models in to production, NeurlIPS conference, model management, Devops and much more.…
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Episode 1: Setting the scene
19:52
19:52
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19:52
This episode of Data Science in Production, focuses on the problems in Data Science and the rise of the Machine Learning Engineer.Par Data Science In Production
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