๐ง ๐ง๐ต๐ฒ ๐ ๐ฎ๐ถ๐ป๐๐ฒ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐ง๐ฟ๐ฎ๐ฝ: ๐ช๐ต๐ ๐ฌ๐ผ๐๐ฟ ๐๐ง ๐ฆ๐๐๐๐ฒ๐บ๐ ๐๐ฟ๐ฒ ๐ ๐ผ๐ฟ๐ฒ ๐๐ถ๐ธ๐ฒ ๐ฃ๐น๐ฎ๐ป๐๐ ๐ง๐ต๐ฎ๐ป ๐ฆ๐๐ผ๐ป๐ฒ๐
๐ง ๐ง๐ต๐ฒ ๐ ๐ฎ๐ถ๐ป๐๐ฒ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐ง๐ฟ๐ฎ๐ฝ: ๐ช๐ต๐ ๐ฌ๐ผ๐๐ฟ ๐๐ง ๐ฆ๐๐๐๐ฒ๐บ๐ ๐๐ฟ๐ฒ ๐ ๐ผ๐ฟ๐ฒ ๐๐ถ๐ธ๐ฒ ๐ฃ๐น๐ฎ๐ป๐๐ ๐ง๐ต๐ฎ๐ป ๐ฆ๐๐ผ๐ป๐ฒ๐
After years of watching organizations struggle with outdated systems, I’ve written about a pattern we all know too wellโthe maintenance trap in IT.
Here’s the uncomfortable truth: We’ve all seen those systems that haven’t been updated in years. Aging interfaces, accumulating bugs, mounting security risks. We assess the cost of updates, weigh the business value, and often decide to “just skip this one.”
๐๐๐ ๐ต๐ฒ๐ฟ๐ฒ’๐ ๐๐ต๐ฎ๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐: ๐ง๐ต๐ฒ ๐ด๐ฎ๐ฝ ๐ด๐ฟ๐ผ๐๐. ๐ง๐ต๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐ ๐ถ๐๐ ๐ฒ๐ ๐ฝ๐น๐ผ๐ฑ๐ฒ๐. ๐ง๐ต๐ฒ ๐ธ๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ ๐ฒ๐๐ฎ๐ฝ๐ผ๐ฟ๐ฎ๐๐ฒ๐. ๐๐ป๐ฑ ๐๐๐ฑ๐ฑ๐ฒ๐ป๐น๐, ๐๐ฒ’๐ฟ๐ฒ ๐๐ฟ๐ฎ๐ฝ๐ฝ๐ฒ๐ฑ.
Now, in the age of AI, something interesting is happening. The game is changing in two directions:
โ AI coding agents are making updates easier and more affordable than ever โ ๏ธ But we’re also introducing new subsystems (AI models) with their own rapid lifecycle challenges
In my latest article, I dive into: โข Why software systems need constant care (like plants, not stones) โข How cloud and AI are transforming the maintenance landscape โข The unique challenges of maintaining AI models with weekly version updates โข Why evolutionary architectures and automated testing are non-negotiable
๐ง๐ต๐ฒ ๐บ๐ฎ๐ถ๐ป๐๐ฒ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฎ๐ฝ ๐ต๐ฎ๐๐ป’๐ ๐ฑ๐ถ๐๐ฎ๐ฝ๐ฝ๐ฒ๐ฎ๐ฟ๐ฒ๐ฑโ๐ถ๐’๐ ๐ฒ๐๐ผ๐น๐๐ฒ๐ฑ. ๐๐ป๐ฑ ๐๐ผ ๐บ๐๐๐ ๐๐ฒ.
๐ Read the full article below …
I’m particularly curious: How are you handling model evaluation and testing in your AI systems? What tools and approaches are working for you?
Let’s discuss. Drop your thoughts in the comments or reach out directly. This is a challenge we’re all navigating together.
#AI #SoftwareEngineering #ITMaintenance #CloudComputing #DevOps #EnterpriseArchitecture #TechnicalDebt #AWS #MachineLearning
Cross-posted to LinkedIn