An ML engineer needs custom Python libraries available in SageMaker processing jobs, training jobs, and pipelines. Which option provides that capability with the LEAST implementation effort?

Building a custom Docker image with the necessary libraries and pushing it to Amazon ECR is the most efficient and scalable solution. SageMaker natively supports using custom ECR images for processing, training, and pipeline steps, ensuring all environments consistently have the required dependencies. This approach minimizes implementation effort compared to manual installations or external orchestration. Manually installing libraries inside default containers is not persistent across job runs and is cumbersome. Using a SageMaker notebook instance for job execution is not a scalable or production-ready solution for pipelines and processing jobs. Executing code externally on EC2 instances adds unnecessary complexity and overhead, defeating the purpose of SageMaker's managed services.