Sold by: MphasisÂ
Deployed on AWS
Descriptive analysis and visualization of key variables from the US Basic Monthly CPS (Current Population Survey).
Overview
The Current Population Survey (CPS) is a monthly survey of households conducted by the United States Census Bureau. It provides a comprehensive data, across geography and demographics, on the labor force, employment, wages, and job characteristics. This solution provides descriptive analysis and easy-to-understand visualization of the Basic Monthly CPS dataset: number of households, members per household, unemployment rate, employment population ratio, labor force participation, family income, non-response rate, employment details (hourly/weekly earnings, major occupation, industry) across geography (state, CSA, CBSA, county) and demographics (education, gender, race, age). This solution can assist government departments, policy consultants, and businesses to quickly generate key insights relevant to understand the U.S. economy.
Highlights
- Descriptive analysis and easy-to-understand visualization of key variables like number of households, members per household, unemployment rate, employment population ratio, labor force participation, family income, non-response rate, employment details (hourly/weekly earnings, major occupation, industry) across geography (state, CSA, CBSA, county) and demographics (education, gender, race, age).
- This solution can assist government departments, policy consultants, and businesses to quickly generate key insights relevant to understand the U.S. economy.
- Mphasis HyperGraf is an Omni-channel customer 360 analytics solution. Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $500.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $500.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $500.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $500.00/host/hour |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $500.00/host/hour |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the Version 1.1 of the algorithm
Additional details
Inputs
- Summary
The input must be a '.zip' file. Each zipped file corresponds to the CPS data collected for one month. Link to download the Basic Monthly CPS data: https://www.census.gov/data/datasets/time-series/demo/cps/cps-basic.html . This solution is optimized for the data dictionary provided in this link: https://www2.census.gov/programs-surveys/cps/datasets/2021/basic/2021_Basic_CPS_Public_Use_Record_Layout_plus_IO_Code_list.txtÂ
- Input MIME type
- text/csv, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/US-Current-Population-Survey-Analysis/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/US-Current-Population-Survey-Analysis/tree/main/Input
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
HSR.health's Hurricane Health Risk Index utilizes social, environmental, economic, and health conditions data to identify the health and medical needs of the population impacted by a hurricane - and in advance of landfall.
Aggregated health and social factors that influence risk from exposure to wildland fires. Data is curated from authoritative sources including the U.S. CDC and Census Bureau.
HSR.health's Wildland Fire (Wildfire) Health Risk Index utilizes social, environmental, economic, and underlying health conditions data from the US Census Bureau and US CDC to identify in advance the health and medical needs of a wildfire-impacted population.
5G Technology Market Insights By Component (Hardware, Software, Services), By Communication Type (Enhanced Mobile Broadband, Massive Internet of Things, Mission Critical Services), By Technology (Radio Access Technologies (RAT), Wi-Fi, High-Speed Package Access (HSPA), Global System for Mobile (GSM), Worldwide Interoperability for microwave access (WiMAX)), By Application (IT & Telecom, Consumer Electronics, Industrial Automation, Automotive, Aerospace & Defense, Healthcare, Others) 2023–2032
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.