This post is co-written by Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, DeepRacer SME at Accenture. The creation of a scalable and hassle-free data science environment is key. It can take a considerable amount of time to launch and configure an environment tailored for a specific use case and even harder to onboard colleagues to collaborate. According to Accenture, companies that manage to efficiently scale AI and ML can achieve nearly triple the return on their investments. Still, not all companies meet their expected returns on their AI/ML journey.

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise machine learning (ML) environments. In this second and final part, we provide instructions for deploying the solution from the source code GitHub repository to your account or accounts and experimenting with the delivered SageMaker notebooks. The provided CloudFormation templates provision all the necessary infrastructure and security controls in your account. An Amazon SageMaker Studio domain is also created by the CloudFormation deployment process. The following diagram shows the resources and components that are created in your account.

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill your organization's operational and security requirements. Amazon SageMaker and Studio provide a wide range of specialized functionality for building highly secure, scalable, and flexible MLOps platforms to cover your model deployment use cases and requirements. Three SageMaker services, SageMaker Pipelines, SageMaker Projects, and SageMaker Model Registry, build a foundation to implement enterprise-grade secure multi-account model deployment workflow.

It seems almost preordained at this point: Thou Shalt Run Thy Data Science Environment On a Cloud-Native Kubernetes Platform. This is 2020, after all. How else could it possibly run? But Tyler Whitehouse, a data scientist who worked at DARPA and IARPA, and his associates from Johns Hopkins University have a very different view on how to manage and distribute resources for data scientists. It does feature containers, but it doesn't involve Kubernetes. To hear Whitehouse tell it, the whole data science community has zigged, without ever considering whether they should have zagged.

How long does it take you to install your complete GPU-enabled deep learning environment including RStudio or jupyter and all your packages? And do you have to do that on multiple systems? In this blog post series I'm going to show you how and why I manage my data science environment with GPU enabled docker containers. How are you managing your data science stack? I was never really satisfied in how I did it.