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Comet Office Hours: Recap for June 6, 2021

Welcome to another recap of the Comet ML Office Hours, powered by The Artists of Data Science!

This past Sunday (June 6th), we dug into some really interesting ideas and technical processes, including what to do when you feel overwhelmed by the plethora of resources for a given task, what advice you’d give to yourself if you could travel back in time, and how to approach reading along your data science journey.

As always, we’d love to see any and all of you at these hourlong sessions—so feel free to register for upcoming Office Hours sessions here.

Without further ado, here’s a quick recap of just a couple of the most interesting conversations we had last week, as well as a list of some of the resources that were mentioned throughout the event.

Lessons to your previous self

Host Harpreet Saota kicked things off by posing a question to the full group—knowing what you know now, if you could go back to the beginning of your data science journey, what advice would give your previous self.

Plenty of interesting responses here, including:

  • Find a way to contribute back to the community from the beginning
  • Learn the statistics that underpin data science work
  • Invest time in learning how to communicate about what you do
  • Don’t be afraid to share your perspective and what you’re learning

The short clip below includes a really powerful reflection by Krzysztof Ograbek on the importance of contributing to the larger community—which naturally, as Comet’s Head of Community, is something I care deeply about.

Getting started with the ML lifecycle

This past week’s conversation wasn’t all philosophical, however. Attendee Bhavika Chavda, who just started an internship as part of a data science degree program, noted that she’d been tasked with building an ML model…the problem is, she’s never worked with ML specifically and isn’t sure where to start.

What followed was a broad outlining of the ML experimentation lifecycle. While, of course, every process and stage of the lifecycle couldn’t be covered fully, Harpreet did a nice job of carving out some of the primary stages:

  • Defining the problem and its scope
  • Creating an initial analysis plan
  • Exploratory data analysis and dataset preparation
  • Feature selection (and engineering)
  • Creating a baseline model
  • Revisiting data groupings
  • Experimenting with increasingly complex models
  • Tuning hyperparameters
  • Working with more advanced modeling techniques (i.e. ensemble models)

What Harpreet outlined is something near and dear to our hearts here at Comet: Experiment Management. For a deeper dive into this topic, be sure to check out our 3-part webinar series + ebook titled “Experiment Management 101”, where our team explores all the key things you’ll need to consider when planning and executing your ML experiments.

But for now, here’s a look at the exchange between Harpeet and Bhavika:

Resources Mentioned

In addition to the wonderful back-and-forths throughout the session, there were a whole lot of interesting resources mentioned, both by Harpreet and many of the attendees. Here’s a quick list, in case you’d like to check out what is capturing the community’s attention.

Enjoy the Conversations Above? Join Us!

We run these virtual Office Hours every Sunday at 12pm ET (New York, NY). Completely free to attend and participate, and we’d love to see any and all of you there, help address any questions you might have, and just hang out and talk all things data science and machine learning!

Register for Comet Office Hours


One last thing…

We recently launched The Comet Newsletter, which offers a weekly inside look at all things data science and ML, featuring expert takes and perspective from our team. We have big things planned for both Office Hours and the newsletter, so be sure to subscribe if you haven’t already!

Subscribe to the Comet Newsletter


Austin Kodra

Austin Kodra

Austin Kodra is the Head of Community at Comet, where he works with Comet's talented community of Data Scientists and Machine Learners.
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