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Data Advocacy, Machine Learning Testing, and Data Teams with Aurimas Griciunas
Manage episode 360903472 series 3390724
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With over a decade in data-related roles, working with Data Science, Data Engineering, Machine Learning, and MLOps Engineering, Aurimas Griciūnas is a great role model for many people wanting to learn more about data. The author of the SwirlAI newsletter with over 7000 subscribers, Aurimas' posts with visuals explaining data have reached many more people on LinkedIn.
In this episode, Aurimas shares valuable insights about what working with data means. Is it just a trend to be data-driven, or, a reality? Are all the data products big data products? Who and how should help define good business success metrics? Tune in to learn the answers to these questions and much more.
Find Aurimas on:
- LinkedIn: https://www.linkedin.com/in/aurimas-griciunas/
- Twitter: https://twitter.com/Aurimas_Gr
- SwirlAI newsletter: https://www.newsletter.swirlai.com
Mentions and resources:
- Chat GPT: https://chat.openai.com/
- Tools to help with data quality:
- Great Expectations: https://greatexpectations.io
- dbt-expectations: https://github.com/calogica/dbt-expectations
- On techniques for testing Machine Learning Models:
- A/B testing: https://en.wikipedia.org/wiki/A/B_testing
- Multi-arm bandits: https://en.wikipedia.org/wiki/Multi-armed_bandit
Follow Quality Bits host Lina Zubyte on:
- Twitter: https://twitter.com/buggylina
- LinkedIn: https://www.linkedin.com/in/linazubyte/
- Website: https://qualitybits.tech/
Follow Quality Bits on your favorite listening platform and Twitter: https://twitter.com/qualitybitstech to stay updated with future content.
If you like this podcast and would like to support its making, feel free to buy me a coffee: https://www.buymeacoffee.com/linazubyte
Thank you for listening!
章
1. Data Advocacy, Machine Learning Testing, and Data Teams with Aurimas Griciunas (00:00:00)
2. Motivation of spreading knowledge on data topics (00:03:54)
3. Data-driven companies: trend or reality? (00:07:50)
4. How could we increase data quality? (00:14:18)
5. Machine learning models and open-source aspect (00:23:41)
6. Testing machine learning or data products (00:27:03)
7. Business metrics: who defines those and what makes sense? (00:32:48)
8. Advice for building high-quality products and teams (00:38:27)
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