Step-by-step guide to set it up in your environment — Joint post with Prayag Singh. ML teams need the flexibility to choose notebooks or full-fledge IDE when working on a project. They may even use multiple IDEs in the same project. It’s a bit like climbing a mountain with the appropriate equipment. …

Code-server in 5 mins in your Studio Lab environment — Amazon SageMaker Studio Lab gives you free access to AWS compute resources to quickly start learning and experimenting with ML. It is simple to setup and allows you to run notebooks on CPU or GPU instances. Studio Lab is based on JupyterLab but you can also run VS Code in…

Making SageMaker Pipelines more resilient with retry policies — Yesterday I used SageMaker Pipelines to automate the workflow of a forecasting project. I launched 3 concurrent pipeline executions to train the model on different time horizons. Think predicting at 1 day, 1 week, 1 month. After a while, 2 executions failed. …

Step-by-step guide to scaling an enterprise ML platform on AWS. — Joint post with Nivas Durairaj The Gutenberg printing press was revolutionary in its time. Suddenly, publishers could print thousands of book pages per day when compared to a few handwritten pages. It enabled a rapid dissemination of knowledge in Europe and opened the era of Renaissance. Today, large enterprises need…

Steps and considerations when rolling out Studio in an enterprise — Often in large enterprises, ML platform admins need to balance governance and compliance requirements with the need for ML teams to quickly access working environments, and scaffolding to operationalize their solutions. In SageMaker terms, this translates into accessing secure, well-governed working environments with Studio, and provisioning templated MLOps projects with…

Step-by-step guide to using MLflow with SageMaker projects — Earlier this year, I published a step-by-step guide to deploying MLflow on AWS Fargate, and using it with Amazon SageMaker. This can help streamline the experimental phase of an ML project. In this post, we will go a step further and automate an end-to-end ML lifecycle using MLflow and Amazon…

Step-by-step guide to SageMaker Studio management with AWS Service Catalog portfolios — In most large enterprises, standardizing, provisioning, and ensuring governance of ML environments is the responsibility of central IT teams. I have recently published a guide to continuous delivery of custom images IT teams can use when setting up SageMaker Studio for their end-users. In this post, we will go a…

Step-by-step guide to Triton deployment on Amazon ECS using the AWS CDK — NVIDIA Triton Inference Server is an open-source software ML teams can deploy their models with. It supports model formats from Tensorflow, PyTorch, ONNX, other popular frameworks, and provides a set of features to manage them. Concurrent model execution and dynamic batching are 2 features you may find interesting in Triton…

Step-by-step guide to continuous delivery of Custom Images in a Studio domain — Amazon SageMaker Studio is a fully integrated IDE unifying the tools needed for managing your ML projects and collaborating with your team members. Alongside providing pre-built images for running your notebooks, SageMaker Studio allows you to create containers with your favourite libraries and attach them as custom images to your…