How to Achieve Robust, Efficient, & Effective Enterprise LLMs

Wallaroo empowers enterprises to deploy, manage, and scale GenAI and LLM models running with ease and flexibility within their data and AI ecosystem , enabling faster time to value and optimal infrastructure utilization.

Launching Effective LLMs Efficiently & Reliably

The Challenges:

Unrealized Time to Value/ROI
Engineering overhead/complexity
Inference Infrastructure cost and availability (GPUs)
Security, Compliance, Bias Risks

Best Practices for Effective LLMs

Speed, Quality, & Quantity

Focus on Time to Value: Leverage ecosystem integrations for quick deployment, monitoring, and iteration of production LLMs.
Establish Tight Feedback Loops: Continuous monitoring and iteration to maximize performance and reduce exposure to bias, and privacy risks without disruption.
Use Automation to Scale Efficiently: Create repeatable industrial grade inference operations for LLMs with modularity, flexibility, and minimal infrastructure complexity

Realize Robust, Efficient, Lower Cost LLM Inference

Wallaroo Allows You To:

LLM Monitoring - Wallaroo LLM listeners™

Solution includes LLM deployed as a single inference pipeline in Wallaroo, as well as automated monitoring metrics and alerting with a vetted set of LLM listeners.

Deploy the model of your choice on any infrastructure architecture to any endpoint with centralized automated management for scale and faster time to value.
Inference logs from model deployments available in Wallaroo Dashboard.
Automated orchestration for scale. Create repeatable industrial grade inferencing with minimal engineering overhead and dependencies.
Continuous monitoring and feedback loops reduce exposure to performance, bias, and privacy risks without disruption delivering faster time to value.

LLM Validation with RAG (Retrieval Augmented Generation)

Solution includes LLM deployed as a single inference pipeline in Wallaroo, as well as RAG.

PlatForm

Solutions

Resources