Title
Observe.AI cuts machine learning costs over 50% with load-testing framework on AWS
Observe.AI Cuts Costs by Over 50% with Machine Learning on AWS Skip to main content Filter: All English Contact us AWS Marketplace Support My…
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Description
Observe.AI, a conversation intelligence platform using a 30-billion-parameter contact center LLM to analyze customer interactions, developed and open-sourced the One Load Audit Framework (OLAF), integrated with Amazon SageMaker, to automatically identify bottlenecks and performance issues in its ML services and predict data load capacity. By fine-tuning SageMaker instance sizes with OLAF, Observe.AI cut ML deployment costs by over 50%, reduced infrastructure sizing time from one week to a few hours, and enabled on-demand scaling to support a 10x increase in data load.
…for boosting contact center performance through live conversation intelligence. Utilizing a robust 30-billion-parameter contact center large language model (LLM) and a generative AI engine, Observe.AI extracts valuable insights from every customer interaction. Trusted by companies, Observe.AI is a valued partner in accelerating positive r…
…ageMaker instance sizes with OLAF while maintaining a constant data input load, we optimized costs for our ML models deployments by over 50 percent. This process ensured the best return on investment." Previously, Observe.AI dev…
Company
Observe.AI
Observe.AI Cuts Costs by Over 50% with Machine Learning on AWS Skip to main content Filter: All English Contact us AWS Marketplace Support My…
Industry
Technology & Software
…-demand scaling to support a tenfold growth in data load size. About Observe.AI Observe.AI is a solution for boosting contact center performance through live conversation intelligence. Utilizing a robust 30-billion-parameter contact center large language model (LL…
Deployment model
Cloud
…ptimal return on investment through fine-tuning its infrastructure was key, and the business wanted a solution compatible with its existing Amazon Web Services (AWS) environment. "We sought a more straightforward method to identify the optimal infrastructure…
Problem
Observe.AI's ML engineers struggled to accurately predict whether its ML system could handle a tenfold increase in data load when moving models from research to production, while needing to manage latency and control costs.
…data load, corresponding to the tenfold rise in conversations processed daily. Our ML engineers and scientists faced challenges in accurately predicting this capability when transitioning models from research to production." The company sought to deploy a larger ML model in production for enhanced accu…
Solution
Observe.AI built and open-sourced the One Load Audit Framework (OLAF), integrated with Amazon SageMaker, Amazon SQS, and Amazon SNS, to load-test ML services and identify bottlenecks, latency, and throughput under static and dynamic data loads.
…eMaker , a service that builds, trains, and deploys ML models for any use case, OLAF identifies bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. The framework also seamlessly incorporates ML performance testing into the soft…
Technology
Amazon SageMaker, Amazon SQS, Amazon SNS, AWS
…on investment. Aashraya Sachdeva Staff Engineer, Machine Learning at Observe.AI AWS Services Used Amazon SageMaker The next generation of Amazon SageMaker is the center for all your data, analyt…
Headline outcome
…ageMaker instance sizes with OLAF while maintaining a constant data input load, we optimized costs for our ML models deployments by over 50 percent. This process ensured the best return on investment." Previously, Observe.AI dev…
Implementation approach
Observe.AI launched OLAF in 2022, then iteratively added Amazon SageMaker multi-container deployment and batch inferencing support, followed by Amazon SQS and SNS integrations; it is now used by dozens of ML engineers and researchers for testing and predicting data loads.
…within Amazon SNS. Outcome | Optimizing Costs and Boosting Developer Efficiency Launched in 2022, OLAF by Observe.AI is now actively employed by dozens of ML engineers and researchers for testing and predicting data loads. By using OLAF, Observe.AI has cut LLM costs by conducting load tests on Amazon…
Business value
Using OLAF, Observe.AI cut ML deployment costs by over 50%, reduced the time to determine the right instance configuration from about a week to a few hours, and can now scale to support a tenfold increase in data load.
…ebugging systems. Aashraya notes. "Because OLAF is tightly integrated with AWS, it now only takes developers a few hours to determine the proper instance for use, a task that used to take one week. As a result, developers can allocate more time to testing data loads and creati…
…to testing data loads and creating new features." With the integration of OLAF, Observe.AI can scale its services to accommodate a tenfold increase in data load. The company can now conduct stress testing more easily and accurately, providin…
AI capabilities
Conversational AI
…for boosting contact center performance through live conversation intelligence. Utilizing a robust 30-billion-parameter contact center large language model (LLM) and a generative AI engine, Observe.AI extracts valuable insights from every customer interaction. Trusted by companies, Observe.AI is a valued partner in accelerating positive r…
Deployment options
cloud
…ptimal return on investment through fine-tuning its infrastructure was key, and the business wanted a solution compatible with its existing Amazon Web Services (AWS) environment. "We sought a more straightforward method to identify the optimal infrastructure…
Capture details
- Captured
- 25 Sept 2026, 05:33 UTC
- Extractor
- fetch-strip@1
- Snapshot hash
- 68c35dce1a6a1628059d59a9444e6456c25e3eb51c017555d9d7b8a111c9f985