Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!

Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.

Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪

🔗 Verify Certificate:

Validation Number: 74c5808986a64b84addcaa9db725b8bd

Verification Link: https://aws.amazon.com/verification

#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps

Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.

Building software with AI requires more than a good prompt.

I've been developing my own Autonomous Engineering Harness to structure how I build software with AI.

The idea is simple: instead of relying on AI to generate code one task at a time, I wanted a more structured process that connects specifications, task planning, implementation, code review, and QA.

The harness organizes the workflow into clear stages:

- Specifications: define what needs to be built.

- Task planning: break requirements into manageable units.

- Implementation: coordinate AI-assisted development.

- Review: check the result against the intended requirements.

-QA: validate the software before considering the work complete.

The goal isn't to make AI autonomous for the sake of it. It's to make AI-assisted engineering more structured, repeatable, and easier to validate.

I'm applying this approach to my own projects and continuing to refine the framework as I learn what works in practice.

I wrote more about the approach, the engineering decisions behind it, and how the framework works on my website.

Read the full story: https://yitsymosqueda.com/autonomous-engineering

I'd carry acceptance criteria from the spec into QA as explicit checks, with a result attached to each one. When the spec changes mid-build, does the workflow flag which completed tasks and QA results need to be revisited?