Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit, developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As a data scientist, you may want to showcase your findings for a dataset, or deploy a trained model. Streamlit applications are useful for presenting progress on a project to your team, gaining and sharing insights to your managers, and even getting feedback from customers.

This post is co-written with Stephen Aylward, Matt McCormick, Brianna Major from Kitware and Justin Kirby from the Frederick National Laboratory for Cancer Research (FNLCR). Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. Like the fully featured Amazon SageMaker Studio, Studio Lab allows you to customize your own Conda environment and create CPU- and GPU-scalable JupyterLab version 3 notebooks, with easy access to the latest data science productivity tools and open-source libraries. Moreover, Studio Lab free accounts include a minimum of 15 GB of persistent storage, enabling you to continuously maintain and expend your projects across multiple sessions and allowing you to instantly pick up where your left off and even share your ongoing work and work environments with others. A key issue faced by the medical image community is how to enable researchers to experiment and explore with these essential tools.

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. In Studio, you can load data, adjust ML models, move in between steps to adjust experiments, compare results, and deploy ML models for inference. The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework to create AWS CloudFormation stacks through automatic CloudFormation template generation. A stack is a collection of AWS resources, that can be programmatically updated, moved, or deleted. AWS CDK constructs are the building blocks of AWS CDK applications, representing the blueprint to define cloud architectures.

Informatica has launched a cloud-based development and data science framework, called INFACore, that promises to simplify the process of composing data pipelines for building and deploying machine learning models in Amazon SageMaker Studio. Powered by Informatica's Intelligent Data Management Cloud, INFACore is described as an intelligent headless data management platform for developers, data scientists, and data engineers. Simplifying the development of complex data pipelines, INFACore can turn thousands of lines of code into a single function that can be deployed into applications using a native UI supported on Amazon SageMaker Studio, the company said. INFACore went into a beta stage in May and is now generally available. Integration between INFACore and other cloud platforms besides AWS is anticipated at some point.

Cloud security at AWS is the highest priority. Amazon SageMaker Studio offers various mechanisms to protect your data and code using integration with AWS security services like AWS Identity and Access Management (IAM), AWS Key Management Service (AWS KMS), or network isolation with Amazon Virtual Private Cloud (Amazon VPC). Customers in highly regulated industries, like financial services, can set up Studio in VPC only mode to enable network isolation and disable internet access from Studio notebooks. You can use IAM integration with Studio to control which users have access to resources like Studio notebooks, the Studio IDE, or Amazon SageMaker training jobs. A popular use case is to restrict access to the Studio IDE to only users from inside a specified network CIDR range or a designated VPC.

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10 times. Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive. You can perform all machine learning (ML) development activities including notebooks, experiment management, automatic model creation, debugging, and model and data drift detection within Studio. In this post, we discuss how to launch Studio from external applications using presigned URLs.

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to obtain Amazon SageMaker access policies to provision Studio separately. SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist with the ability to build, train, and deploy ML models quickly. Studio is a web-based integrated development environment (IDE) for ML that lets you build, train, debug, deploy, and monitor your ML models.

Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don't know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time. Historically, AWS has presented its services as cloud-only.

Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don't know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time. Historically, AWS has presented its services as cloud-only.

Today, I'm extremely happy to announce the availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that accelerates your machine learning workflows with one-click access to popular model collections (also known as "model zoos"), and to end-to-end solutions that solve common use cases. In recent years, machine learning (ML) has proven to be a valuable technique in improving and automating business processes. Indeed, models trained on historical data can accurately predict outcomes across a wide range of industry segments: financial services, retail, manufacturing, telecom, life sciences, and so on. Yet, working with these models requires skills and experience that only a subset of scientists and developers have: preparing a dataset, selecting an algorithm, training a model, optimizing its accuracy, deploying it in production, and monitoring its performance over time. In order to simplify the model building process, the ML community has created model zoos, that is to say, collections of models built with popular open source libraries, and often pretrained on reference datasets.