3 real-world examples of AI/ML Engineers -> their stack, challenges, and day-to-day focus!
“The hard part of AI/ML engineering is not the model → it’s curating, labeling, cleaning, and understanding data.”
This has been my experience. At scale, finding high quality human data is a challenge.
Thanks a lot for the feature Gregor!
Reading it, I've also learned from the insights Shivam and Bhuvaneshwaran have shared, loved the article.
My pleasure Alex, thank you for sharing your insights with all of us! They will definitely help many. And glad you learned something as well!
“The hard part of AI/ML engineering is not the model → it’s curating, labeling, cleaning, and understanding data.”
This has been my experience. At scale, finding high quality human data is a challenge.
Thanks a lot for the feature Gregor!
Reading it, I've also learned from the insights Shivam and Bhuvaneshwaran have shared, loved the article.
My pleasure Alex, thank you for sharing your insights with all of us! They will definitely help many. And glad you learned something as well!