In a recent survey, it was found that 90% of data scientists have never been presented with a practical example of unit tests in production data science applications. This statistic highlights a significant gap in the implementation of unit tests within MLOps flows. The survey also revealed that many data scientists are unsure about when and how often these unit tests should be executed, as well as who should be responsible for checking them. These findings underscore the need for clearer guidelines and more robust examples of unit testing in the field of data science. As the industry continues to evolve, addressing these challenges will be crucial for improving the reliability and efficiency of MLOps processes.
Jachimike O (Ø,G) (꧁IP꧂)
@JachimikeO
·
17h After purchasing a 1-year data plan that suspiciously depleted in 4 months, I reached out for answers. Rather than investigate, I got these lazy, dismissive replies from FM & RI — advising me to “check background apps” like I’m not tech-literate.The screenshots below show the
Data Wolf
@0xDataWolf
·
Apr 14 If you are frequently getting timed out during analysis that’s because there are more and more rows from faster chainsThis is 1 month of dataCost of managing data is passed onto the analytics layer which then be passed on to users in the future
Shaun Newman Podcast
@SNewmanPodcast
·
Jan 28 Their data system wasn’t just broken—it was designed to confuse.• 72-hour delays in basic statistics
• ICU numbers underreported by 20%
• 40% of cases had zero contact tracing
• 17 separate health systems that couldn’t communicatePerfect for hiding inconvenient truths.
Dr Shivam Sharma
@DocShivSharma
·
Feb 25 FOI reveals 1,527 locum shifts over the last 6 months went unfilled at
@LG_NHS
where the rates are abysmalClear evidence of doctor substitution with PAs and ACPs working doctor shiftsPatient safety is being compromisedSign the pledge to scrap the cap:https://bit.ly/3Er45dH
Aloha Protocol
@AlohaProtocol
·
Apr 17 Every minute of bad data costs you.
And most teams don’t even know it’s broken.Bar Moses, CEO of
@montecarlodata
, reveals how top data teams are slashing incident time:
From 20 hours to 3.Because what you can’t measure, you can’t fix.Data trust starts with one
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