Openlayer

@mwseibel, thank you for the hunt! Hi Product Hunt 👋👋👋 We are Vikas, Gabe and Rish from Openlayer. We’re building a next-gen monitoring and eval platform that allows developers to keep track of how their AI is performing beyond token cost and latency. Why? There is no comprehensive toolkit for AI reliability that gives developers insight into how and why their AI fails. Traditional software has a myriad of standardized tests and monitors, but ML development has lagged behind, often hinging on intuition and post-release user feedback. What does Openlayer do? 🧠 Intelligent testing: You can setup all sorts of tests in Openlayer, ranging from data quality and drift checks to model performance expectations using rule-based metrics and GPT evaluation. 🔍 Log and debug: Log your production requests, and run your tests on them to make sure they’re performing for your users as expected. 🚨 Alerts: Get proactive notifications in Slack and email when something’s amiss, and detailed reports that let you drill down into what exactly went wrong. 💼 For pre-release and production: Whether your model is in development or shipped, our platform stands watch, offering continuous analysis. 🚀 Seamless integration: Deploy our monitors with just a few lines of code and watch as Openlayer becomes the guardrail for your AI models. It’s developer-first, hassle-free, and provides real-time insights. 🌏 Beyond LLMs: While we have specialized tools for language models, Openlayer’s scope includes a wide range of AI tasks. Tabular data, classification, regression models – you name it, we can test it. 📊 Subpopulation analysis: Understand how your model interacts with different data demographics. Pinpoint where your AI excels and where improvement is needed. 🔒 Security-conscious: We’re SOC 2 Type 2 compliant, with on-premise hosting options for those with stringent security requirements. Using this toolset, you can set up all sorts of guardrails on your AI. For example, you might be building an application that relies on an LLM, and want to guarantee the output doesn’t contain PII or profanity. You can define a test for this in Openlayer. Or, you might be developing a classification model that predicts fraud on your platform, and you want to guard against data drift over time. You can do this with Openlayer. Product Hunt offer: For those willing to give feedback on their experience using the product, we’re giving out free (limited edition 😉) swag. Send an email to founders@openlayer.com after trying out the product to schedule a call! We're eager for you to explore Openlayer and shape its evolution. Join for free and start testing your models and data! ◾ Sign up for free: https://app.openlayer.com/ ◾ Join our community: https://discord.gg/t6wS2g6MMB ◾ Curious about diving deeper into Openlayer? Reach out to us at founders@openlayer.com to request an upgrade to your workspace.

Congrats on the launch Vikas and team! I haven't looked much into LLM Monitoring tools but it's something on my radar for the new year. I hear the term "eval" a lot these days but how do we concretely do this if we are not a ML Engineer or Researcher? It would be great if you had some tutorials on how to do eval for the non-initiated.

Openlayer's focus on observability and evaluation for LLM and machine learning products is timely and vital. Best wishes for contributing to the advancement of AI technologies! How does Openlayer ensure the accuracy and reliability of its evaluation tools?