Comet Office Hours: Recap for June 13, 2021
Welcome to another recap of the Comet ML Office Hours, powered by The Artists of Data Science!
Unfortunately, I wasn’t able to make it to this past week’s session, but I had a chance to watch the conversation, and I’m excited to share a couple of my favorite points of discussion, as well as some resources the group shared.
While there were plenty of excellent moments to consider for this write-up—including dealing with obstacles in the post-bootcamp job hunt, understanding where data for ML comes from, and more—I’ll share two other threads that focused touched on a couple of key questions…one for data science learners, and another for current practitioners.
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.
Keeping up your momentum while learning
Office Hours regular Asha jumped into the mix with an important question that all aspiring data scientists—and frankly all learners—must confront at some point:
How do you keep up the momentum while learning something new, especially when there are so many possible threads to explore.
I found Harpreet’s response below to be quite thoughtful and convincing—especially his note that, while he was growing his skillset, he was “ruthless with [his] time”.
There were a couple other thoughts form the group on this, as well:
- Crafting your environment in such a way that encourages consistency over time in your learning is key
- Similarly, putting too much stock in the idea of “momentum” and high-pace progress can be detrimental in the long run.
The short clip below includes Harpreet’s initial response, but there were plenty of great thoughts throughout the discussion.
Data Science, ML, and Product-Market Fit
This past week’s conversation wasn’t all philosophical, however. Attendee and Data Scientist at Humu Mark Freeman had some very important observations about how being a data scientist or machine learner at an early-stage startup can create a unique set of opportunities and challenges.
Specifically, working for a younger, smaller organization can provide the opportunity to think deeply about core business problems like product-market fit, connect your work as a data scientists to the context of a product’s (or company’s) growth, and ultimately be instrumental in shaping how a company approaches their ML projects and infrastructure.
Conversely, Mark also highlighted a few specific challenges, including the limited ability of early-stage startups to invest in infrastructure and other ML resources, as well as the fact that product decisions that rely on complicated ML pipelines are difficult to justify in an early-stage roadmap.
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.
- Striking Thoughts, Bruce Lee
- Real World AI, Alyssa Simpson Rochwerger & Wilson Pang
- The Google Resume, Gayle Laakmann McDowell
- How to Solve It, G. Polya
- Lean Launchpad, Stanford
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!
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!