Data Science at Home

268 Episodes

1. MLOps: what is and why it is important Part 2 (Ep. 152)

   Published: 5/19/2021
2. MLOps: what is and why it is important (Ep. 151)

   Published: 5/11/2021
3. Can I get paid for my data? With Mike Andi from Mytiki (Ep. 150)

   Published: 4/28/2021
4. Building high-growth data businesses with Lillian Pierson (Ep. 149)

   Published: 4/19/2021
5. Learning and training in AI times (Ep. 148)

   Published: 4/13/2021
6. You are the product [RB] (Ep. 147)

   Published: 4/11/2021
7. Polars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146)

   Published: 4/8/2021
8. Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)

   Published: 3/26/2021
9. Pandas vs Rust (Ep. 144)

   Published: 3/19/2021
10. Concurrent is not parallel - Part 2 (Ep. 143)

    Published: 3/13/2021
11. Concurrent is not parallel - Part 1 (Ep. 142)

    Published: 3/10/2021
12. Backend technologies for machine learning in production (Ep. 141)

    Published: 3/2/2021
13. How to reinvent banking and finance with data and technology (Ep. 139)

    Published: 2/15/2021
14. What's up with WhatsApp? (Ep. 138)

    Published: 2/7/2021
15. Is Rust flexible enough for a flexible data model? (Ep. 137)

    Published: 2/1/2021
16. Is Apple M1 good for machine learning? (Ep.136)

    Published: 1/25/2021
17. Rust and deep learning with Daniel McKenna (Ep. 135)

    Published: 1/18/2021
18. Scaling machine learning with clusters and GPUs (Ep. 134)

    Published: 12/31/2020
19. What is data ethics? (Ep. 133)

    Published: 12/19/2020
20. A Standard for the Python Array API (Ep. 132)

    Published: 12/8/2020