Customer Contact Central

Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

JULY 29, 2022

In this post, we address the analytics and ML platform team as a consumer in the data mesh. The platform team sets up the ML environment for the data scientists and helps them get access to the necessary data products in the data mesh. When querying data via Athena, the intermediate results are also saved in Amazon S3.

Scripts

Scripts Analytics Accountability Engineering

Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning

SEPTEMBER 19, 2023

The steps involved are as follows: The financial analyst poses questions via a platform such as chatbots. The platform uses a framework to determine the most suitable multi-modal agent tool to answer the question. Once identified, the platform runs the code that is linked to the previously identified tool.

Marketing

Marketing APIs Engineering Analytics

Automate your time series forecasting in Snowflake using Amazon Forecast

AWS Machine Learning

OCTOBER 4, 2022

At AWS, we sometimes work with customers who have selected our technology partner Snowflake to deliver a cloud data platform experience. Having a platform that can recall years and years of historical data is powerful—but how can you use this data to look ahead and use yesterday’s evidence to plan for tomorrow? Solution overview.

APIs

APIs Analytics Transportation Metrics

Visualize your Amazon Lookout for Metrics anomaly results with Amazon QuickSight

AWS Machine Learning

AUGUST 18, 2022

The solution is a combination of AWS services, primarily Lookout for Metrics, QuickSight, AWS Lambda , Amazon Athena , AWS Glue , and Amazon S3. An AWS Glue crawler analyzes the metadata, and creates tables in Athena. Use the combination of Athena and AWS Glue to discover and prepare the data for QuickSight. Choose Next.

Metrics

Metrics Scripts Accountability Best practices

Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

AWS Machine Learning

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. In this post, we cover the benefits for SaaS platforms to integrate with SageMaker, the range of possible integrations, and the process for developing these integrations.

SaaS

SaaS Accountability APIs Engineering

Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store

AWS Machine Learning

AUGUST 1, 2022

The advantage of Feature Store is that the feature engineering logic is authored one time, and the features generated are stored on a central platform. Feature Store automatically creates an AWS Glue Data Catalog for the offline store, which enables us to run SQL queries against the offline data using Amazon Athena. References.

APIs

APIs Engineering Entertainment Healthcare

Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

JULY 29, 2022

For more information, refer to How JPMorgan Chase built a data mesh architecture to drive significant value to enhance their enterprise data platform. We address data consumption by the analytics and ML CoE with Amazon Athena and Amazon SageMaker in part 2 of this series. Create the Athena workgroup consumer-workgroup.

Government

Government Banking Analytics Accountability

Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker

AWS Machine Learning

SEPTEMBER 29, 2022

Amazon SageMaker is a fully managed ML platform in the cloud that equips our enterprise customers with tools and resources to establish cost allocation measures and improve visibility into detailed cost and usage by your teams, business units, products, and more. Visualize the CUR using Amazon Athena and Amazon QuickSight.

Enterprise

Enterprise APIs Government Accountability

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning

APRIL 16, 2024

Solution overview With SageMaker Studio JupyterLab notebook’s SQL integration, you can now connect to popular data sources like Snowflake, Athena, Amazon Redshift, and Amazon DataZone. Create an Athena connection Athena is a fully managed SQL query service from AWS that enables analysis of data stored in Amazon S3 using standard SQL.

Airlines

Airlines Management Engineering Accountability

MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

JUNE 24, 2022

In addition to SageMaker services, data scientists can use other services to process the data, such as Amazon EMR , Amazon Athena , and AWS Glue , with notebooks stored and versioned in AWS CodeCommit repositories (see the following figure). The next step is to onboard new ML use cases and teams to the platform. Repeatable phase.

Enterprise

Enterprise Engineering Accountability APIs

New Amazon HealthLake capabilities enable next-generation imaging solutions and precision health analytics

AWS Machine Learning

NOVEMBER 15, 2022

Intelerad and Arterys are among the launch partners utilizing HealthLake Imaging to achieve higher scalability and viewing performance for their next-generation PACS systems and AI platform, respectively. HealthLake Analytics automatically normalizes raw health data from multiple disparate sources (e.g.

Analytics

Analytics Healthcare Engineering APIs

What are AWS Data Lakes?

ConvergeOne

MARCH 23, 2021

A Data Lake provides organizations with a centralized repository for a wide variety of data forms, located in a central platform that supports structured, semi-structured, and unstructured data. Services like EMR can run your dig data applications or Amazon Athena for ad-hoc real-time interactive analytics. Basically, it is a mess!

Analytics

Analytics CRM

Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

MAY 5, 2023

For users who are not familiar with the service offerings that are provided on the cloud platform, they may provide different generic ways and descriptions when searching for such a diagram. The following figure shows an example diagram that illustrates an orchestrated extract, transform, and load (ETL) architecture solution.

Engineering

Engineering APIs Scripts Enterprise

Build a news-based real-time alert system with Twitter, Amazon SageMaker, and Hugging Face

AWS Machine Learning

JULY 15, 2022

Users rely on platforms like Facebook and Twitter to consume news. The availability of thousands of pre-trained NLP models in hundreds of languages and with support for multiple frameworks provided by the community in platforms like Hugging Face Hub. Today, social media is a huge source of news. This is a large pre-trained model (1.6

Engineering

Engineering APIs Accountability Government

Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning

APRIL 24, 2024

The device metrics are stored in an Athena DB named "iot_ops_glue_db" in a table named "iot_device_metrics". The solution is integrated with AWS services such as Lambda for running code in response to API calls, Athena for querying datasets, OpenSearch Service for searching through knowledge bases, and Amazon S3 for storage.

APIs

APIs Engineering Metrics Management

The Best 15 Customer Experience Software Tools to Elevate the Customer Experience

Netomi

MARCH 10, 2022

Not all customer experience platforms are the same – some are more focused on a website’s visual design elements, while others are primarily focused on offering various forms of surveys. Sunshine, the “foundation beneath Zendesk,” is an open and flexible platform, built natively on AWS. Zendesk Sunshine’s key features.

Customer Experience

Customer Experience Analytics Surveys Feedback

The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

AWS Machine Learning

MARCH 14, 2024

We formulated a Retrieval-Augmented-Generation (RAG) solution that would allow the PGA TOUR to create a prototype for a future fan engagement platform that could make its data accessible to fans in an interactive fashion in a conversational format. The SQL is run by Amazon Athena to return the relevant data.

Engineering

Engineering APIs Technology Finance

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning

JUNE 22, 2023

is the leading cross-platform global game company that provides gambling products and services. Data preparation To extract data efficiently for training and testing, we utilize Amazon Athena and the AWS Glue Data Catalog. This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W).

Automotive

Automotive Engineering Metrics Finance

Get insights on your user’s search behavior from Amazon Kendra using an ML-powered serverless stack

AWS Machine Learning

MAY 25, 2023

An AWS Glue crawler creates or updates the AWS Glue Data Catalog from the uploaded file in the S3 bucket for an Amazon Athena table. QuickSight uses the Athena table dataset to create analyses and dashboards. For this solution, we use QuickSight for the business intelligence (BI) dashboard and Athena as the data source for QuickSight.

Automotive

Automotive Metrics Analytics APIs

Our top 10 questions answered!

Livepro

OCTOBER 2, 2018

This API will offer our clients the ability to connect the valuable insights with the QuickSight solution through AWS Athena to start producing easy to manage and use Dashboards. Yes, we definitely plan to integrate with other telephony platforms and CRMs. Do you have any plans to integrate with systems other than PureCloud?

APIs

APIs Best practices Management Customer Service

Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning

DECEMBER 14, 2023

Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. Load processed features for a specified date range using SQL from an Amazon Athena table, then train and deploy the job recommender model. path_suffix='.parquet', path_suffix='.parquet',

Engineering

Engineering Scripts Technology Analytics

Cost-effective data preparation for machine learning using SageMaker Data Wrangler

AWS Machine Learning

NOVEMBER 7, 2022

This blog will not compare different platforms or services for EDA, but understand different steps in EDA, their cost considerations, and how Data Wrangler facilitates EDA in a cost-effective way. Managing the EDA platform and the underlying compute infrastructure involves expertise, effort, and cost. Labor cost and expertise.

Best practices

Best practices Engineering Analytics Benchmark

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

NOVEMBER 17, 2022

As data is growing at an exponential rate, organizations are looking to set up an integrated, cost-effective, and performant data platform in order to preprocess data, perform feature engineering, and build, train, and operationalize ML models at scale. Create an IAM group called data-platform-group. Choose Grant. About the Authors.

Engineering

Engineering Big data Accountability Analytics

Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning

FEBRUARY 29, 2024

AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres to organizational compliance and security standards. Deutsche Bahn has been at the forefront in adopting AI, using SageMaker Studio as a key AI platform.

APIs

APIs Construction Transportation Analytics

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

AWS Machine Learning

NOVEMBER 15, 2023

The use of this BI tool, with its native integrations to the existing data repositories made accessible by Amazon Athena , made the creation of visualizations to display the large-scale data relatively straightforward, and enabled self-service BI.

Analytics

Analytics Interactive Voice Response Average Handle Time Contact Center

Identify cybersecurity anomalies in your Amazon Security Lake data using Amazon SageMaker

AWS Machine Learning

DECEMBER 20, 2023

Use Amazon Athena to query structured OCSF log data from Amazon Simple Storage Service (Amazon S3) through AWS Glue tables managed by AWS LakeFormation. Implement the provided IAM policy and corresponding IAM trust policy for your SageMaker Studio Notebook instance to access all the necessary data in S3, Lake Formation, and Athena.

Engineering

Engineering Accountability Analytics Construction

How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning

MARCH 8, 2023

Marubeni Power International Inc (MPII) owns and invests in power business platforms in the Americas. Amazon Athena to provide developers and business analysts SQL access to the generated data for analysis and troubleshooting. MPII plans to build additional digital solutions to support new power business platforms.

Marketing

Marketing Analytics APIs Engineering

Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

AWS Machine Learning

NOVEMBER 24, 2023

JuMa is a service of BMW Group’s AI platform for its data analysts, ML engineers, and data scientists that provides a user-friendly workspace with an integrated development environment (IDE). These platforms were too limited regarding CPU, GPU, and memory to allow the scalability of AI at BMW Group.

Engineering

Engineering Analytics Government Self service

What Is Decision Intelligence: Best Examples and Business Benefits?

SmartKarrot

FEBRUARY 18, 2022

Decision intelligence helps your organization evolve with decision-making using advanced technologies like ML , AI, Natural language queries, intelligent apps, and more to create comprehensive platforms. Their platform uses decision intelligence to suggest winning investment strategies for customers. Like what you are reading?

Banking

Banking Finance Healthcare SaaS

Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock

AWS Machine Learning

FEBRUARY 9, 2024

Challenges An AI platform administrator needs to provide standardized and easy access to FMs to multiple development teams. To query and visualize the data stored in Amazon S3, you have different options, including S3 Select , and Amazon Athena and Amazon QuickSight. Let’s go through the details of the architecture.

SaaS

SaaS APIs Government Accountability

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

AWS Machine Learning

MARCH 22, 2023

Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. You can import data from multiple data sources, such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Amazon EMR , and Snowflake. Matt Marzillo is a Sr. Partner Sales Engineer at Snowflake.

APIs

APIs Engineering Analytics Scripts

Import data from over 40 data sources for no-code machine learning with Amazon SageMaker Canvas

AWS Machine Learning

APRIL 6, 2023

Now, you can import data in-app from popular relational data stores such as Amazon Athena as well as third-party software as a service (SaaS) platforms supported by Amazon AppFlow such as Salesforce, SAP OData, and Google Analytics. In the first example, we demonstrate how to import data through Athena. Let’s get started.

SaaS

SaaS Engineering Analytics Management

Capture public health insights more quickly with no-code machine learning using Amazon SageMaker Canvas

AWS Machine Learning

JUNE 28, 2023

Previous to AWS Dan built enterprise SaaS platforms and time-series risk models used by institutional investors to manage risk and construct portfolios. You can edit the dataset in a spreadsheet, or directly inside Canvas using the user interface.

Construction

Construction Government SaaS Enterprise

Customer Contact Central

Build and train ML models using a data mesh architecture on AWS: Part 2

Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

Trending Sources

Automate your time series forecasting in Snowflake using Amazon Forecast

Visualize your Amazon Lookout for Metrics anomaly results with Amazon QuickSight

Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store

Build and train ML models using a data mesh architecture on AWS: Part 1

Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

MLOps foundation roadmap for enterprises with Amazon SageMaker

New Amazon HealthLake capabilities enable next-generation imaging solutions and precision health analytics

What are AWS Data Lakes?

Build an image search engine with Amazon Kendra and Amazon Rekognition

Build a news-based real-time alert system with Twitter, Amazon SageMaker, and Hugging Face

Enhance conversational AI with advanced routing techniques with Amazon Bedrock

The Best 15 Customer Experience Software Tools to Elevate the Customer Experience

The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

Get insights on your user’s search behavior from Amazon Kendra using an ML-powered serverless stack

Our top 10 questions answered!

Streamlining ETL data processing at Talent.com with Amazon SageMaker

Cost-effective data preparation for machine learning using SageMaker Data Wrangler

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

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

Identify cybersecurity anomalies in your Amazon Security Lake data using Amazon SageMaker

How Marubeni is optimizing market decisions using AWS machine learning and analytics

Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

What Is Decision Intelligence: Best Examples and Business Benefits?

Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Import data from over 40 data sources for no-code machine learning with Amazon SageMaker Canvas

Capture public health insights more quickly with no-code machine learning using Amazon SageMaker Canvas

Stay Connected