AWS Certified AI Practitioner: Build AI/ML Pipelines & MLOps on AWS

What I Learned From AWS Certified AI Practitioner

Just wrapped up the AWS Certified AI Practitioner cert. No fluff — this one’s solid if you want a clear grasp on AI, ML, and generative AI basics on AWS.

Key Takeaways

• Learned the fundamentals of AI and ML, including how to pick the right AI/ML tech for different use cases.

• Understood how core AWS services like Amazon SageMaker, AWS Lambda, AWS Glue, and Amazon S3 fit into AI/ML workflows.

• Got a good look at responsible AI use — a must in today’s world.

• Now I know how to build AI/ML pipelines using AWS SageMaker Pipelines and train machine learning models efficiently.

• Gained hands-on understanding of MLOps concepts — continuous integration and continuous delivery (CI/CD) for machine learning, model monitoring, and version control.

• My background in data engineering helped a lot — prepping and processing data with tools like AWS Glue and storing it in Amazon S3 for model training.

• Thanks to a mentor who helped me translate theory into practical pipeline design and deployment.

Why This Matters

AI and ML aren’t just buzzwords. With AWS services like SageMaker for training and deployment, Glue for ETL, and CloudWatch for monitoring, you can automate and manage models in production — that’s the essence of MLOps.

Knowing how to responsibly apply these technologies on AWS means building smarter, scalable AI systems that drive real business value.

For anyone serious about cloud, AI, or data — this cert is a strong foundation. If you’ve worked on data pipelines before, this is the natural next step to level up with MLOps.