Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Cash Flow Statement Extractor
By:
Latest Version:
v1.1
This solution extracts all tabular data pertaining to the Cash Flow Statements in a company's annual report.
Product Overview
This solution takes an annual report in a digital PDF format as input, and returns the JSON data corresponding to the extracted tabular data from the report's Cash Flow statements. The solution utilizes computer vision techniques to identify the pages with tables, as well as text-based classification techniques to determine the relevance of the pages in question. This is done extremely quickly, rendering it unnecessary for an individual to go through hundreds of pages before finding the required information.
Key Data
Version
By
Type
Model Package
Highlights
The Cash Flow Data Extractor uses text classification techniques to identify the pages containing information about Cash Flows. It then utilizes table detection and extraction algorithms to narrow the search to only those pages that contain tabular data, before finally retrieving it.
This solution can be used in industries like consulting, banking, financial services, insurance, retail, healthcare, pharmaceuticals, manufacturing, airlines, etc to automate processes like financial spreading, vendor/merchant risk assessment, fundamental analysis etc.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$10.00/hr
running on ml.m5.2xlarge
Model Batch Transform$20.00/hr
running on ml.m5.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge Vendor Recommended | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Model input and output details
Input
Summary
Usage Methodology for the algorithm: 1) The input must be 'Input.zip' file. 2) The zip file should contain Input file which has a .pdf file. 3) The PDF file should be a non-encrypted digital file with content not as scanned images. 4) Name of the folder inside the zip file should be “Input” which is case-sensitive 5) check the instructions and sample endpoint in the sample jupyter file provided.
Input file structure:- Input.zip |--Input |--sample_financial_report.zip
Limitations for input type
only one (annual report) pdf in a zip file
Input MIME type
application/zipSample input data
Output
Summary
Unzip the output.zip file. That will contain the extracted "Cash Flow" csv files .
Output MIME type
text/plain, application/zipSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Cash Flow Statement Extractor
For any assistance reach out to us at:
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