AWS Partner Network (APN) Blog
Category: Amazon Machine Learning
Migrate On-Premises Machine Learning Operations to Amazon SageMaker Pipelines for Computer Vision
When migrating on-premises MLOps to Amazon SageMaker Pipelines, customers often find it challenging to monitor metrics in training scripts and add inference scripts for custom machine learning models. Learn how Mission Cloud implemented an end-to-end SageMaker Pipeline to build the workflow of model development to production, accelerating their customer’s computer vision model production process. SageMaker Pipelines is a workflow orchestration tool for building ML pipelines with CI/CD capabilities.
Leveraging MLOps on AWS to Accelerate Data Preparation and Feature Engineering for Production
Feature engineering is a critical process in which data, produced by data engineers, are consumed and transformed by data scientists to train models and improve their performance. Learn how to accelerate data processing tasks and improve collaboration between data science and data engineering teams by applying MLOps best practices from Data Reply and leveraging tools from AWS. Data Reply is focused on helping clients deliver business value and differentiation through advanced analytics and AI/ML on AWS.
Enabling Machine Learning Adoption with Genpact’s Analytics Maturity Meter and AWS
Organizations realize the value of data and analytics for their businesses, but not all of them have been successful in defining a mature analytics vision and strategy. By cross-leveraging experience in process management, safeguarding data, and rich analytics practices, Genpact has developed an analytics maturity assessment framework known as the Analytics Maturity Meter (AMM). Learn how this solution evaluates a company’s current capabilities in data, process, technology, talent, and enterprise leadership.
Building a Cloud-Native Architecture for Vertical Federated Learning on AWS
Federated learning is a distributed machine learning technique that doesn’t require data to be centralized, and it doesn’t disclose data to other parties while building the model. Learn how DOCOMO Innovations focuses on federated learning, and particularly vertical FL because it has potential to get better model performance by collaborating with other data providers. DOCOMO Innovations has been investigating the VFL algorithm and its implementation on AWS for real-world scenarios.
Successful Decentralized Clinical Trials: A True Possibility with AWS in the Post-Pandemic Era
Decentralized clinical trials (DCTs) put the patient at the center of the trial experience and incorporate digital technologies like AI/ML to address the challenges associated with traditional clinical trials. DCTs can reshape workflows across the clinical lifecycle—from trial design and patient recruitment to evidence generation. Explore key challenges addressed by DCTs and how SourceFuse is leveraging AWS to build the right solutions for its clients to transform clinical research.
How MoEngage Built a Multi-Tenant Data Lake and Analytics Platform on AWS
Learn how MoEngage built a digital customer engagement SaaS solution at a large scale by adopting various strategies and best practices to ingest, store, and analyze multi-tenant data inside their data lake and analytics platform. MoEngage’s AI-Powered Campaign Optimization provides analytics and enhanced insights to MoEngage’s customers (tenants). It delivers a configurable analytics experience that makes it easy for customers to create, use, and distribute tailored campaigns.
Discover and Protect Sensitive Data with HCLTech’s DataPatrol Framework Built with Machine Learning on AWS
It’s critical to identify and protect the sensitive data collected from any unauthorized disclosure, and it’s the responsibility of every organization to effectively discover, control, and manage their sensitive data footprints and comply with relevant data protection laws. Learn how HCLTech‘s DataPatrol framework accomplishes critical tasks in the lifecycle of sensitive documents and improves sensitive data discovery and governance across your AWS environment.
How Quantiphi Breaks Through Machine Learning Bottlenecks with NeuralOps
Organizations have matured and have overcome the initial hurdles of proving the capabilities of AI. The challenge now is operationalizing AI and building engineering excellence to successfully adopt and manage machine learning at scale. Learn how Quantiphi assisted Venterra Realty in bringing in the best ML solution development and deployment practices through NeuralOps—a framework built on Amazon SageMaker.
Demystifying Natural Language Processing and Machine Learning with Amazon Comprehend
With Amazon Comprehend, the implementation of natural language processing and machine learning has become a simple, routine task. Organizations no longer have to spend hours trying to pick the right algorithm, as Amazon Comprehend automatically selects the best ones for any given use case. To explore this benefit, learn about an implementation of Amazon Comprehend for risk prediction and sentiment analysis on the observations in clinical trials sites.
Nordcloud’s Automated Solution for Computer Vision Applications at the Edge Using AWS Panorama
In computer vision applications, the transmission of video data to the cloud for analysis can result in added delays due to various contributing factors such as queuing, propagation, and network latency. Learn how the Nordcloud team, in collaboration with AWS, has designed a “Computer Vision at the Edge” solution based on AWS Panorama. It caters to organizations seeking low-latency decision making without the burden of managing complex technology.