AWS Partner Network (APN) Blog

Tag: ML Models

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How to Simplify Machine Learning with Amazon Redshift

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

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How TCS is Delivering Remote Virtual Inspections for Insurers Enabled by AWS Services

By avoiding inspections, insurers lose the opportunity to adequately consider all factors during underwriting and take on more risk. TCS has built a virtual inspection solution on AWS that is customizable to various inspection use cases, such as home insurance inspections and claims, auto claims damage assessment and estimation, and mortgage inspection. This post provides an overview of the TCS virtual inspection solution, describes the high-level architecture, and explores the potential business benefits for insurers.

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Optimize the Cost of Running DataRobot Models by Deploying and Monitoring on AWS Serverless

Operationalizing machine learning models can be a challenge due to lack of established ML architecture and its integration with the existing landscape. DataRobot integrates with AWS and provides the flexibility for a model trained in DataRobot to be deployed on AWS services with centralized model governance, management, and monitoring. Learn how the DataRobot AutoML platform orchestrates the complete model development and training lifecycle.

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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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Taming Machine Learning on AWS with MLOps: A Reference Architecture

Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). How can data science and analytics teams tame complexity and live up to the expectations placed on them? MLOps provides some answers. Hear from AWS Premier Consulting Partner Reply how you can “glue” the various components of MLOps together to build an MLOps solution using AWS managed services.

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How Pr3vent Uses Machine Learning on AWS to Combat Preventable Vision Loss in Infants

Scaling doctors’ expertise through artificial intelligence (AI) and machine learning (ML) provides an affordable and accurate solution, giving millions of infants equal access to eye screening. Learn how Pr3vent, a medical AI company founded by ophthalmologists, teamed up with AWS Machine Learning Competency Partner Provectus to develop an advanced disease screening solution powered by deep learning that detects pathology and signs of possible abnormalities in the retinas of newborns.

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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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Amazon Fraud Detector Can Accelerate How AI is Embedded in Your Business

Online fraud is estimated to be costing businesses billions of dollars a year. As Fraudsters evolve new behaviors to get around preventive measures, businesses need a strategy that enables them to be responsive to new problems as they emerge. Learn how Inawisdom uses Amazon Fraud Detector to accelerate how AI can be embedded in a company’s strategy. What makes machine learning more flexible is its focus on identifying general patterns by looking at lots of examples.

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