Several People Are Tuning: Data and Machine Learning at Slack: Slack has only been publicly available for a little over two years, but during that time we have been able to create one of the most interesting data sets in the world about how teams form, grow, and collaborate in order to get things done. Our next great challenge is to find ways to leverage what we’ve learned in order to help people find the information they need faster and to help large organizations work together more effectively. Although our mission is broad, we are still at the very start of our journey; our machine learning team was only created six months ago, and our data team just six months before that. I would like to talk for a bit about what that journey has been like: how we hired our team, how we chose our tools and developed our initial data infrastructure and machine learning models, and how we’ve framed the product problems that we’re trying to solve.

1. Several People Are Tuning:
Data and ML at Slack

2. About Me
● Slack’s Head of Data
Engineering
● Used to work at Cloudera,
Google, other places
● Wrote popular tweet that
I’m sort of tired of talking
about
● Only owns one hat

3. Our Mission:
To make people’s working
lives simpler, more pleasant,
and more productive.

4. Investing in Data
(when you’re not an ad/fintech company)

5. Data Is A Portfolio Business

6. The Vision Thing

7. Org Stuff: Analytics, Data, SLI

8. In The Beginning...

9. Enter Data Engineering
Prod
MySQL (sharded),
Solr, Redis
Third Party
Data Warehouse
S3, Hive, Spark, Presto, MySQL
Webapp
(Apache, PHP)
Sqooper
Logs
(via
Kafka)

10. The Ghost City

11. Staffing Up

12. Sprucing Up The Place

13. The Nature of the Work

14. The Product Problem

15. “We Don’t Sell Saddles”

16. Building A Knowledge Base

17. Weird, Wonderful ML

18. Minimum Viable Data Products

19. Running Experiments

20. Channel Recommendations

21. Data Models for Serving

22. Giant Shoulders

23. Thanks! We’re hiring!