Comet ML Office Hours 3 - 21FEB2021

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February 25th, 2021

1 hr 14 mins 8 secs

Season 9

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About this Episode

Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.

Register for future sessions here: http://bit.ly/comet-ml-oh

Checkout Comet ML by visiting: https://www.comet.ml/

Or on Twitter: https://twitter.com/CometML

Connect with Ayodele

LinkedIn: https://www.linkedin.com/in/ayodeleodubela/

Twitter: https://twitter.com/DataSciBae

Check out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom

[00:01:05] When we talk about data validation, what is it that we mean?

[00:03:19] A data sheet? What is that?

[00:05:30] What’s a good approach to asking questions for a data science project?

[00:10:41] Sampling is important

[00:17:54] Where does data validation fall in the data science lifecycle?

[00:18:31] Where in the pipeline do I perform cross-validation?

[00:21:00] How do algorithms know which content to push to you and how can I affect the content being pushed my way?

[00:26:26] There is a lack of transparency when it comes to these algorithms

[00:30:50] Some more excellent discussion around ethics in machine learning

[00:32:52] Ayodele drops some sage insight on how machine learning algorithms are used and the ethics of it all

[00:36:20] How to go from data to decisions

[00:43:10] What exactly is an insight?

[00:47:09] What comes first: the question or the data?

[00:53:22] How do you create a narrative around your analysis?

[01:02:09] How do you talk about the narrative in your project?

[01:05:14] Eliminating data feeds that are wasting money, why are you collecting data that you don’t use?

[01:11:23] What’s your favorite data science book?

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