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The panelists discuss ways to improve as developers. Are better tools the solution, or can simple changes in mindset help? And what practices are already here, but not yet universally adopted?
Legacy applications actually benefit the most from concepts like a Minimum Viable Product (MVP) and its related Minimum Viable Architecture (MVA). Once you realize that every release is an experiment in value in which the release either improves the value that customers experience or doesn’t, you realize that every release, even one of a legacy application, can be thought of in terms of an MVP.
This article covers the benefits of streaming-first infrastructure for two scenarios of real-time ML: is online prediction, where a model can receive a request and make predictions as soon as the request arrives, and continual learning, when machine learning models are capable of continually adapting to change in data distributions in production.
Establishing SRE in a software delivery organization typically requires a socio-technical transformation. Operations teams need to learn how to provide a scalable SRE infrastructure to enable development teams to run their services efficiently. This paper presents how agile coaching has been employed to run an SRE transformation in a 25-teams strong product delivery organization.
Erin Schnabel discusses how application metrics align with other observability and monitoring methods, from profiling to tracing, and the limits of aggregation.
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InfoQ Homepage News Amazon Announces New Capabilities on Local Environments for SageMaker Canvas and Pipelines
Aug 23, 2022 2 min read
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Amazon is announcing multiple capabilities for SageMaker, including expanded capabilities to better prepare and analyze data for machine learning, faster onboarding with automatic data import from local disk in SageMaker Canvas, and the testing of machine learning workflows in local environments for SageMaker Pipelines.
Business analysts can create reliable ML forecasts on their own with SageMaker Canvas, a visual point-and-click interface, without any prior machine learning knowledge or coding knowledge. SageMaker Canvas makes it simple to access and merge data from various sources, clean data automatically, and create ML models to get precise predictions with only a few clicks.
Users of SageMaker Canvas can import data from a number of sources, including local disk, Amazon S3, Amazon Redshift, and Snowflake. As of right now, users can upload datasets from their local disk directly to SageMaker Canvas without consulting their administrators because the necessary permissions are already enabled. When creating a domain, SageMaker can connect a cross-origin resource sharing (CORS) policy to the built-in Amazon S3 bucket for local file uploads by enabling the "Enable Canvas permissions" setting for administrators. If administrators don’t want domain users to upload local files automatically, they can choose to disable this feature. In all AWS areas where SageMaker Canvas is supported, faster onboarding with automatic data onboarding from local disk is now possible.
Additional features for data preparation and analysis in Amazon SageMaker Canvas include the ability to specify multiple sample sizes for datasets as well as the ability to replace missing values and outliers. With just a few clicks, SageMaker Canvas makes it simple to access and mix data from many sources, automatically clean data, and create ML models that produce precise predictions.
Finally using SageMaker Pipelines, you can create machine learning pipelines that directly integrate with SageMaker. SageMaker Pipelines now allows you to build and test pipelines on a local computer. With this release, you may locally check the compliance of your Sagemaker Pipelines scripts and parameters before executing them on SageMaker in the cloud.
The following steps are supported by Sagemaker Pipelines Local Mode: processing, training, transform, model, condition, and fail. You have the freedom to specify different entities in your machine learning workflow thanks to these stages. You may quickly and effectively troubleshoot script and pipeline definition issues by using Pipelines local mode. By upgrading the session, you may easily transition your workflows from local mode to Sagemaker's managed environment.
The new capabilities add to the range of data preparation capabilities and advanced data transformations supported by Amazon SageMaker.
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