AWS Partner Network (APN) Blog

Tag: ML Models

Palantir-AWS-Partners

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.

Read More
Reply-AWS-Partners

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More
Accenture-AWS-Partners

Optimizing Supply Chains Through Intelligent Revenue and Supply Chain (IRAS) Management

Fragmented supply-chain management systems can impair an enterprise’s ability to make informed, timely decisions. Accenture’s Intelligent Revenue and Supply Chain (IRAS) platform integrates insights generated by machine learning models into an enterprise’s technical and business ecosystems. This post explains how Accenture’s IRAS solution is architected, how it can coexist with other ML forecasting models or statistical packages, and how you can consume its insights in an integrated way.

Read More

Training Multiple Machine Learning Models Simultaneously Using Spark and Apache Arrow

Spark is a distributed computing framework that added new features like Pandas UDF by using PyArrow. You can leverage Spark for distributed and advanced machine learning model lifecycle capabilities to build massive-scale products with a bunch of models in production. Learn how Perion Network implemented a model lifecycle capability to distribute the training and testing stages with few lines of PySpark code. This capability improved the performance and accuracy of Perion’s ML models.

Read More
TCS-AWS-Partners

Intelligent Call Routing Using Amazon Fraud Detector and Amazon Connect

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as online payment fraud and the creation of fake accounts. Learn how APN Premier Consulting Partner TCS has been integrating Amazon Fraud Detector to detect spam calls and route them efficiently using Amazon Connect. Used together, these AWS services can distinguish your genuine customers from spam or fraudulent callers.

Read More