AWS Partner Network (APN) Blog

Category: Amazon Machine Learning

Machine Learning-3

Training Machine Learning Models in Pharma and Biotech Manufacturing with Bigfinite

While market adoption of machine learning varies across industry segments, healthcare and life sciences have lots of opportunity to explore. Pharmaceutical and biotech industries, specifically, have low utilization of the large volumes of data they collect and store for regulatory purposes. Bigfinite is an APN Standard Technology Partner that offers a number of ML solutions to enhance utilization and management of such data in a way that adheres to Good Manufacturing Practice (GMP) in the cloud.

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Machine Learning-3

4 Steps to Train and Deploy Machine Learning Models on AWS Using H2O

H2O is an open source data machine learning platform that provides a flexible, user-friendly tool to help data scientists and machine learning practitioners. It was created by H2O.ai, an APN Advanced Partner with the AWS Machine Learning Competency. In this post, we look at setting up an H2O cluster, import data from Amazon S3, create an AWS Lambda deployment package from the model, and finally deploy a RESTful endpoint. Following these steps, you can migrate your H2O Flows to AWS in about 10 minutes.

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Machine Learning Competency Launch

Customer Success Stories from AWS Machine Learning Competency Partners

At the AWS London Summit, we announced the new AWS Machine Learning Competency Program for APN Consulting Partners. Launch Partners in this program have deep expertise and proven customer success in Machine Learning on AWS, and we are excited to showcase some of their stories. The AWS Competency Program helps customers identify and choose the top APN Partner for their AWS projects and workloads.

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SageMaker

Integrating with Amazon SageMaker: Using Built-In Algorithms from External Applications

We are often asked how to integrate software with Amazon SageMaker and use the service’s built-in machine learning algorithms. In this post, we discuss how to use the training capabilities of Amazon SageMaker to leverage its built-in algorithms. The types of applications that can integrate with Amazon SageMaker are data science platforms, business intelligence tools, or any application that needs to use machine learning behind the scenes.

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New Training Courses Available: Introduction to Machine Learning & Deep Learning on AWS

AWS Training and Certification offers guidance to APN Partners so you can more effectively help customers leverage the AWS Cloud. We have two new courses to help you learn more about artificial intelligence solutions using AWS: Introduction to Machine Learning web-based training and Deep Learning on AWS instructor-led training. If you are looking to learn […]

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Partner SA Roundup – July 2017

This month, Juan Villa, Pratap Ramamurthy, and Roy Rodan from the Emerging Partner SA team highlight a few of the partners they work with. They’ll be exploring Microchip, Domino, and Cohesive Networks. Microchip Zero Touch Secure Provisioning Kit, by Juan Villa AWS IoT is a managed cloud platform that enables connected devices to easily and […]

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Why Our Customers Love Amazon Machine Learning – A Guest Post from 47Lining

Mick is CEO of 47Lining, an AWS Advanced Consulting Partner in the AWS Partner Network (APN) with the Big Data Competency designation. He holds an AWS Solutions Architect Professional certification. Amazon Machine Learning is a service that provides predictive capabilities, the results of which can be incorporated into a wide variety of downstream applications and […]

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Machine Learning-2

Amazon Machine Learning and Amazon Elastic File System: A Perspective for APN Partners

At the AWS San Francisco Summit on April 9, we announced two new AWS services: Amazon Machine Learning and Amazon Elastic File System (Amazon EFS). In this post, we will discuss some of the ways that you might think about how you can utilize these new services to build your business on AWS. Build smarter applications with Amazon Machine Learning, a predictive analytics service, and take advantage of high availability, shared, easy-to-use file storage for Amazon EC2 applications.

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