Barriers to AI/ML adoption
For many years, AI/ML technology adoption was challenging for many organizations for many reasons. Let me quickly summarize some of them here:
|
Challenge |
Reasons |
|
Expensive infrastructure |
Training ML models on large datasets required a lot of compute, memory, and storage. Multiple iterations of tuning made this whole process very expensive on traditional on-prem infrastructure as all this hardware had to be procured upfront. |
|
Not enough data scientists and ML builders |
Building ML systems required niche skill sets with an understanding of complex ML algorithms. This made it difficult for organizations to easily acquire resources that had all the necessary skill sets to help them build an ML platform. |
|
Tedious and time-consuming processes |
...
