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  • SAS to PySpark: LTIMindtree’s Specialized SLM for On-Premises Migration

    SAS to PySpark: LTIMindtree’s Specialized SLM for On-Premises Migration
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SAS Modernization: Tackling Cost, Complexity, and Security

Enterprises relying on SAS face increasing challenges in today’s data-driven world. High licensing costs, limited cloud compatibility, and scalability constraints make it difficult for organizations to optimize their analytics capabilities. Additionally, migrating SAS workloads to modern platforms like PySpark is complex, requiring expertise in both SAS and big data technologies.

Security concerns further complicate the transition – Generic cloud-based LLMs introduce risks of data exposure and high compute costs, making them unsuitable for enterprises with stringent compliance requirements. These challenges create the need for a specialized, secure, and cost-efficient migration approach.

SAS to PySpark: LTIMindtree’s Specialized SLM for On-Premises Migration

LTIMindtree’s Small Language Model (SLM) is a fine-tuned AI-powered solution designed specifically for SAS-to-PySpark code conversion. Unlike generic LLMs, our on-premises or private cloud-hosted SLM ensures zero data exfiltration, making it ideal for organizations with strict security and governance policies.

By accurately interpreting SAS syntax, macros, and logic, the SLM delivers high-accuracy conversions, minimizing manual intervention and reducing project timelines. Its optimized compute efficiency significantly lowers infrastructure costs, eliminating expensive cloud LLM fees.

SAS to PySpark

Benefits

 

Enhanced Data Security

No risk of accidental data exposure through API calls or external endpoints.

Lower Migration Costs

Eliminates recurring cloud LLM expenses and minimizes SAS licensing dependencies.

Higher Conversion Accuracy

Reduces manual intervention and improves PySpark code performance.

Seamless Maintenance

Easily updated and customized for evolving business needs.

Future-ready Analytics

Enables enterprises to leverage AI, ML, and big data more effectively.

Features

  • On-premises Deployment: The SLM can be deployed directly on a customer-provided Virtual Machine (VM), even without internet connectivity, eliminating data security and privacy concerns.
  • Minimal Resource Footprint: Focused and targeted fine-tuning allows the SLM to operate efficiently, using fewer tokens and reducing computational overheads.
  • Scalability and Control: Customers retain full control over their infrastructure, scaling up or down as needed, without incurring third-party cloud costs.
  • Cost Efficiency: Since the SLM runs on optimized infrastructure and does not require extensive compute resources or data transfer, it proves more economical for lengthy SAS code bases.
Features

Value Proposition

 
Locally Hosted

Locally Hosted

By deploying on a customer’s VM, data never leaves the enterprise network. This simplifies compliance with data governance regulations.

No Data Exfiltration

No Data Exfiltration

All customer data—source code, process logs, and results—remain confined within the customer’s infrastructure.

Reduced Token Usage

Reduced Token Usage

Domain-specific training reduces extraneous prompts and tokens, cutting operational costs for large code conversion tasks.

Easy Maintenance

Easy Maintenance

The model can be regularly updated and patched onsite, ensuring continuous improvements without external dependencies.

Unlock seamless SAS modernization today! Reach out to us.

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