Striveworks Blog The Reason Your AI Program Fails to Sustain Value

When you think of data scientists, what comes to mind? If you're like most people, you probably picture a team of math PhDs training models and conducting experiments-churning through millions of GPU hours to build the next GPT.The reality is that most data scientists spend a fraction of their time on that.Although data scientists are in high demand across sectors, only a fraction of their work hours are spent on traditional data science. Anaconda's 2022 State of Data Science report shows the reality: What we think of as "classical" data science only makes up 34% of data scientists' time. The rest is spent on gruntwork-data preparation, data cleansing, deploying, and similar tasks that eat up hours of brain power. Increasingly, this work happens after models deploy-in the phase of the AI life cycle known as post-production.