A naukri.com initiative
Medium
1M
85
Image Credit: Medium
Machine Learning/LLM Observability at Datadog- My PM Internship experience!
- Datadog LLM Observability helps organizations run large language models (LLMs) is production and at an enterprise scale.
- LLM application workflows can be complex. Detecting errors and latency for troubleshooting can be challenging.
- LLM Observability collects prompts and traces end-to-end context about how your application processed prompt to form the final response.
- It includes operational performance metrics, so you can analyze request volume, application errors, and latency over time.
- LLM Observability’s traces provide a detailed latency breakdown, so you can spot which chain components contributed the most latency.
- Datadog LLM Observability provides out-of-the-box quality checks and custom evaluations to help you monitor the quality of your application's output.
- To help you keep your LLM applications secure, Datadog LLM Observability detects and highlights prompt injections and toxic content in your LLM traces.
- You can filter Traces list by the out-of-the-box Security and Privacy checks to quickly find traces that triggered these signals.
- Datadog LLM Observability gives granular visibility into the behavior of LLM-based applications for actionable insights into their health, performance, and security.
- LLM Observability is now generally available for all Datadog customers.
Read Full Article
5 Likes
For uninterrupted reading, download the app