Remove Categorization Remove Data Ingestion Remove Metadata
article thumbnail

Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

Combining accurate transcripts with Genesys CTR files, Principal could properly identify the speakers, categorize the calls into groups, analyze agent performance, identify upsell opportunities, and conduct additional machine learning (ML)-powered analytics.

article thumbnail

How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning Blog

The ML components for data ingestion, preprocessing, and model training were available as disjointed Python scripts and notebooks, which required a lot of manual heavy lifting on the part of engineers. The initial solution also required the support of a technical third party, to release new models swiftly and efficiently.

DevOps 108
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning Blog

The data scientist discovers and subscribes to data and ML resources, accesses the data from SageMaker Canvas, prepares the data, performs feature engineering, builds an ML model, and exports the model back to the Amazon DataZone catalog. On the Asset catalog tab, search for and choose the data asset Bank.

article thumbnail

List of ETL Tools: Explore the Top ETL Tools for 2025

Pickl AI

Whether you are a data engineer, analyst, or business intelligence professional, understanding these tools can help you make informed decisions for your data integration needs. Apache NiFi Apache NiFi is an open-source data integration tool that provides an intuitive user interface for designing data flows.

ETL 52
article thumbnail

Learnings From Teams Training Large-Scale Models: Challenges and Solutions For Monitoring at Hyperscale

The MLOps Blog

The solution lies in systems that can handle high-throughput data ingestion while providing accurate, real-time insights. Igor Tsvetkov Former Senior Staff Software Engineer, Cruise AI teams automating error categorization and correlation can significantly reduce debugging time in hyperscale environments, just as Cruise has done.

article thumbnail

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Parallel computing Parallel computing refers to carrying out multiple processes simultaneously, and can be categorized according to the granularity at which parallelism is supported by the hardware. The following table shows the metadata of three of the largest accelerated compute instances. 32xlarge 0 16 0 128 512 512 4 x 1.9

ML 89
article thumbnail

Simplify automotive damage processing with Amazon Bedrock and vector databases

AWS Machine Learning Blog

Amazon OpenSearch Service is a powerful, highly flexible search engine that allows you to retrieve data based on a variety of lexical and semantic retrieval approaches. By combining these powerful tools, we have developed a comprehensive solution that streamlines the process of identifying and categorizing automotive damage.