Service in review: Sagemaker Modeling Pipelines - DEV Community

Welcome back to my blog, where I share insights and tips on machine learning workflows using Sagemaker Pipelines. If you're new here, I recommend checking out my first post to learn more about this AWS fully managed machine learning service. In my second post, I discussed how parameterization can help you customize the workflow and make it more flexible and efficient. After using Sagemaker Pipelines extensively in real-life projects, I've gained a comprehensive understanding of the service. In this post, I'll summarize the key benefits of using Sagemaker Pipelines and the limitations you should consider before implementing it. This service is integrated with Sagemaker directly, so the user doesn't have to deal with other AWS services.