2_use_case_name: Modernization Accelerator - International Business Machines (IBM) Cognitive Data Mapper (CDM) and AWS SageMaker+LLAMA
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Business Masterfile Modernization (BMF Mod) project will reproduce the legacy BMF system, written in 60-year-old mainframe technology, into a modern, cloud-based system. BMF Mod will also add additional features to the system as new technology permits.

Note that this software will only be used in an isolated development (sandbox) environment, to assist developers in programming the new modernized system. It will not be promoted into Production. 

These Artificial Intelligence/Machine Language (AI/ML) tools analyze the existing legacy BMF system code, after it has been prepared by other non-AI/ML International Business Machines (IBM) tools, to automatically produce: Business logic in natural language, which describe what actions or data transformations the code performs: Modernized Java code to implement the business rules; Microservice recommendations, meaning that code snippets are grouped together by function and access patterns, to split very large programs that are hard to understand into smaller chunks that can be more easily maintained.

These tools take in code from the existing BMF system, after it has been prepared by other non-AI/ML IBM tools, and analyze the code to produce the business logic, code, and microservice recommendations.
IBM Cognitive Data Mapper (CDM) uses Natural Language Processing (NLP) Machine Learning (ML) models to automatically analyze the source system's data and metadata, map to a target model or business glossary, and/or classify the information. CDM will be used for automated data mapping to automatically identify and establish relationships between data elements in different data sources and source systems.
Amazon Web Services (AWS) SageMaker with LLAMA accepts prepared code from other non-AI/ML IBM tools, then further analyzes the code to extract business logic and convert the code to modern Java.

The tools analyze existing code from the BMF system, which are very large programs, and suggest how to translate the code into smaller, more manageable and understandable business logic, code, and microservices.

All output business logic, code, and microservice recommendations will be reviewed by both IBM and IRS employees prior to being used in development of the new modernized BMF system. The outputs will be put into a spreadsheet that can be shared with subject matter experts, who will then manually check accuracy of the work. Code will also be packaged and extensively tested to ensure accuracy.

These AI/ML tools will accelerate our development process, allowing us to deliver a modernized system a few years faster than otherwise. The output business logic and microservice recommendations are also very useful as documentation artifacts, helping to understand how the BMF system functions.

Approval will allow BMF Mod work to continue. Without approval we would need to extend our modernization plans to account for performing these functions manually.

While these AI/ML tools are only intended to be used by IBM consultants working on the BMF Mod program, its impacts will benefit all BMF stakeholders including IRS business operating divisions, Treasury, and taxpayers.
12_outputs: IBM CDM uses Natural Language Processing (NLP) Machine Learning (ML) models to automatically analyze the source system's data and metadata, map to a target model or business glossary, and/or classify the information. CDM will be used for automated data mapping to automatically identify and establish relationships between data elements in different data sources and source systems.
AWS SageMaker with LLAMA accepts prepared code from other non-AI/ML IBM tools, then further analyzes the code to extract business logic and convert the code to modern Java.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/6/2024
19_date_acq_dev_began: 6/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H5-24-F-00181
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Existing legacy Business Master File code
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.