Automate the development of ML models with Amazon SageMaker lifecycle configurations

In recent years, the landscape of technology and innovation has been significantly shaped by the exponential rise of machine learning (ML) with Amazon SageMaker, a fully managed ML service powered by Amazon Web Services (AWS), becoming a pivotal platform in its acceleration. SageMaker integrates development, training and validation processes into one seamless workflow, making it easier to develop and deploy robust ML models.

SageMaker offers notebook instances in the cloud, providing a collaborative development (dev) environment for building, experimenting and sharing ML models with support for various programming languages and libraries. This experience is enhanced by SageMaker’s lifecycle configurations which allows you to automate tasks and customize notebook instance setups in Amazon SageMaker.

Setting up a dev environment manually is often prone to inconsistencies and errors and can present a barrier to maintaining uniformity in the operating system settings, library installations and access permission. As ensuring a standard setup for various projects is crucial for operational integrity, manual setups can be particularly challenging when configuring multiple SageMaker notebook instances.

The solution: Automation with Amazon SageMaker lifecycle configurations

Baker Tilly has developed a script within Amazon SageMaker lifecycle configurations that automates the process of configuring SageMaker notebook environments. By executing a series of predefined actions upon the creation, start or reboot of the notebook instances, we ensure that each environment adheres to the required standards.

This includes setting operating system (OS) environment variables, installing necessary Python packages with pip, performing system updates with yum install and securely storing GitHub credentials for version control and code management. This capability is a boon for our operations, particularly when onboarding new staff, as it ensures consistency across development environments and significantly reduces the time spent on manual configurations.

Key features and benefits

1. Automated environment setup: