AWS Partner Network (APN) Blog

Category: Amazon SageMaker

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Enabling Data-Centric Artificial Intelligence Through Snowflake and Amazon SageMaker

Data-centric AI (DCAI) has been described as the discipline of systematically engineering the data used to build an AI system. It prescribes prioritizing improving data quality over tweaking algorithms to improve machine learning models. In this post, explore a DCAI solution built on Snowflake and Amazon SageMaker to serve as a factory for predictive analytics solutions. Learn about Snowflake’s integrations with SageMaker and get hands-on resources to help you put these capabilities into practice.

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Implementing a Multi-Tenant MLaaS Build Environment with Amazon SageMaker Pipelines

Organizations hosting customer-specific machine learning models on AWS have unique isolation and performance requirements and require a solution that provides a scalable, high-performance, and feature-rich ML platform. Learn how Amazon SageMaker Pipelines helps you to pre-process data, build, train, tune, and register ML models in SaaS applications. We’ll focus on best practices for building tenant-specific ML models with particular focus on tenant isolation and cost attribution.

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Implementing SaaS Tenant Isolation Using Amazon SageMaker Endpoints and IAM

As multi-tenant SaaS providers look to leverage machine learning services, they must consider how they’ll protect the data that flows in and out of these services from different tenants. Learn how tenant isolation of machine learning services can be achieved using AWS IAM, and how the integration between IAM, Amazon SageMaker, and many other AWS services provide developers with a rich set of mechanisms that can be applied to realize tenant isolation goals.

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How Palantir Foundry Helps Customers Build and Deploy AI-Powered Decision-Making Applications

Leveraging data to make better decisions is critical for driving optimal business outcomes. Palantir empowers organizations to rapidly extract maximum value from one of their most valuable assets—their data. Palantir Foundry solves for the real-world application of AI, and not how it works in the lab. Effective AI is impossible without a trustworthy data foundation, a representation of an institution’s decisions, and the infrastructure to learn from every decision made.

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Digital Visual Inspection and Asset Integrity Management with Wipro’s InspectAI on AWS

Asset integrity management is a key activity for energy companies, and with recent advances in the field of machine learning, specifically computer vision, there are digital technologies that can enhance customers’ existing workflows and help plan preventative work. Learn how Wipro’s visual inspection and integrity management solution, InspectAI, can help customers deploy a cloud-based solution and transform their inspection process on AWS.

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AI-Driven Analytics on AWS Using Tableau and Amazon SageMaker

Organizations that have foresight into their business have a competitive advantage. Advanced analytics that enable foresight have historically required scarce resources to develop predictive models using techniques like machine learning. Traditionally, this is a difficult endeavor, but recent progress in ML automation has made democratization of ML a possibility. Learn about the value of augmenting analytics with ML through the Amazon SageMaker for Tableau Quick Start.

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Machine Learning for Everyone with Amazon SageMaker Autopilot and Domo

Machine learning allows users to drive insights about their business, and the AutoML approach speeds up this process through the automation of ML pipeline steps. Learn how Domo created AutoML capabilities powered by Amazon SageMaker Autopilot, which is a fully managed AWS solution that automatically creates, trains, and tunes the best classification and regression ML models based on the data provided by a customer.

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How to Build and Deploy Amazon SageMaker Models in Dataiku Collaboratively

Organizations often need business analysts and citizen data scientists to work with data scientists to create machine learning (ML) models, but they struggle to provide a common ground for collaboration. Newly enriched Dataiku Data Science Studio (DSS) and Amazon SageMaker capabilities answer this need, empowering a broader set of users by leveraging the managed infrastructure of Amazon SageMaker and combining it with Dataiku’s visual interface to develop models at scale.

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How to Export a Model from Domino for Deployment in Amazon SageMaker

Data science is driving significant value for many organizations, including fueling new revenue streams, improving longstanding processes, and optimizing customer experience. Domino Data Lab empowers code-first data science teams to overcome these challenges of building and deploying data science at scale. Learn how to build and export a model from the Domino platform for deployment in Amazon SageMaker. Deploying models within Domino provides insight into the full model lineage.

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How Provectus and GoCheck Kids Built ML Infrastructure for Improved Usability During Vision Screening

For businesses like GoCheck Kids, machine learning infrastructure is vital. The company has developed a next-generation, ML-driven pediatric vision screening platform that enables healthcare practitioners to screen for vision risks in children in a fast and easy way by utilizing GoCheck Kids’ smartphone app. Learn how GoCheck Kids teamed up with Provectus to build a secure, auditable, and reproducible ML infrastructure on AWS to ensure its solution is powered by highly accurate image classification model.

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