Tag: Machine Learning

With multiple mediums of communication, like social media, television, and OTT, marketers are finding it difficult to understand the factors that impact ad conversions, and provide visibility into marketing campaigns. To address this challenge, Quantiphi worked with Advocado, an advertising focused data platform provider, to develop a solution for ad attribution of web traffic to respective offline and online sources on AWS.

A closed loop assurance system predicts network events, such as faults and congestions, that are highly probable of causing service degradation or interruption, and automatically take preventive actions to avert service disruptions. Learn how Infosys leveraged AWS data streaming, data analytics, and machine learning services to ingest, process, and analyze high volumes of data from disparate sources; and to build ML models to predict network events that cause service degradation.

We are excited to highlight 75 AWS Partners that received new designations in March for our global AWS Competency, AWS Managed Service Provider (MSP), AWS Service Delivery, and AWS Service Ready programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

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.

Healthcare customers use Snowflake to store all types of clinical data in a single source of truth. One method for gaining insights from this data is to use Amazon Comprehend Medical, which is a HIPAA-eligible natural language processing service that uses machine learning to extract health data from medical text. Learn how the Snowflake Data Cloud allows healthcare and life sciences organizations to centralize data in a single and secure location.

We are excited to highlight 72 AWS Partners that received new designations in February for our global AWS Competency, AWS Managed Service Provider (MSP), AWS Service Delivery, and AWS Service Ready programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

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.

To quickly build intelligent data-driven workflows, organizations need business analysts to work with data scientists and development teams to unlock useful insights from unstructured or semi-structured data. Learn how KNIME’s end-to-end data science product portfolio helps bridge the gap between the ideation and productionalization steps of data science projects, while also assisting in the communication of key data science aspects between teams.

Insurance companies need a solution to effectively manage vehicle insurances provided to their end customers, and telematics solutions are used to monitor and manage these fleets. Learn how IBM developed Telematics Hub that focuses on risk analysis, crash and claim management, and more. The solution can be extended to additional use cases and integrate with many third-party IoT devices and applications for vehicle data collection.

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.