AWS Partner Network (APN) Blog
Tag: ML Models
Fast, Accurate, Alternate Credit Decisioning Using ElectrifAi’s Machine Learning Solution on AWS
Infusing machine learning into core business processes such as credit scoring creates a competitive edge for banks and financial services institutions. It does not require a data science team, expertise, or platform rollout. Explore an ML-based credit-decisioning model built by ElectrifAi in collaboration with AWS whose model rapidly determines the creditworthiness of a SME, and data-driven, actionable insights reduce the overall processing cost and are consistent and free from any potential human biases.
Deploy Accelerated ML Models to Amazon Elastic Kubernetes Service Using OctoML CLI
Deploying machine learning (ML) models as a packaged container with hardware-optimized acceleration, without compromising accuracy and while being financially feasible, can be challenging. As machine learning models become the brains of modern applications, developers need a simpler way to deploy trained ML models to live endpoints for inference. This post explores how a ML engineer can take a trained model, optimize and containerize the model using OctoML CLI, and deploy it to Amazon EKS.
Machine Learning Infrastructure for Commercial Real Estate Insights Platform
Learn now Provectus looked into how machine learning models were prototyped and evaluated at VTS, and then delivered a template-based solution enabling their data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. The resulting coherent set of templates, with usage cookbook and extension guidelines, was applied successfully on an ML model that predicted leasing outcomes.
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.
Automating Signature Recognition Using Capgemini MLOps Pipeline on AWS
Recognizing a user’s signature is an essential step in banking and legal transactions, and typically involves relying on human verification. Learn how Capgemini uses machine learning from AWS to build ML-models to verify signatures from different user channels including web and mobile apps. This ensures organizations can meet the required standards, recognize user identity, and assess if further verifications are needed.
Accelerate Your Life Sciences Data Journey with Accenture Intelligent Data Foundation on AWS
Increasing penetration of analytics in the life sciences industry is expected to drive significant growth for businesses in the coming years. Learn about Accenture’s life sciences data and analytics accelerator which enables customers to respond to these challenges and use data for their competitive advantage. Particular focus is given to the commercial domain and use of analytics to increase customer engagement and optimize sales and marketing.
Managing Machine Learning Workloads Using Kubeflow on AWS with D2iQ Kaptain
Kubernetes is hardware-agnostic and can work across a wide range of infrastructure platforms, and Kubeflow—the self-described machine learning toolkit for Kubernetes—provides a Kubernetes-native platform for developing and deploying ML systems. Learn how D2iQ Kaptain on AWS directly addresses the challenges of moving ML workloads into production, the steep learning curve for Kubernetes, and the particular difficulties Kubeflow can introduce.
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.
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.
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.