Kick-start your path to production with a project template — Earlier this year, I published a step-by-step guide to automating an end-to-end ML lifecycle with built-in SageMaker MLOps project templates and MLflow. It brought workflow orchestration, model registry, and CI/CD under one umbrella to reduce the effort of running end-to-end MLOps projects. In this post, we will go a step…

Step-by-step guide to deploying Label Studio on a Notebook Instance — Joint post with Phil Meakins. Few years ago in my data scientist days, I worked on a computer vision project in which my team spent weeks tweaking NN layers and hyperparameters in order to get better performance. It turned out that simply improving the quality of dataset labels was the…

Step-by-step guide to set it up in your environment — Joint post with Prayag Singh and Phil Meakins. 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. …

Setup VS Code in 5 mins in your 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 scaled ML environment provisioning 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 the AWS Service Catalog Factory — 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…