Openlayer is a powerful testing and observability platform for ML. It lets you collaborate with others on finding issues in models and data, debugging them, and committing new versions.
We've been using Openlayer for the past couple of months, and it has been a valuable asset to our team. The platform's timeline feature is excellent for tracking progress, and collaborating with the team is effortless. The Openlayer team is highly responsive to feedback and feature requests, making it a top-notch platform for gaining insights into machine-learning models
I love OpenLayer because it allows not just engineers, but PMs, analysts, and managers to participate in the ML development process. Finally, a way to catch errors before the product gets into the hands of users!
Optimizing an ML model at scale requires a bunch of different tools and lots of work by the engineers + data scientists. Love that Openlayer can do all of this for a company (detect errors, suggest new optimizations, etc.), definitely a game-changer for ML teams! 👏🏾
Very cool approach to ML testing. I like how you track against commits and help define goals as you define the pipeline. One question - how do define the "root cause" that you mention when solving failed goals?
This is awesome! Having a great debugging workspace on par with software engineering debugging has always been a pain point to me when working on finance data and autonomous driving. What are some of the use cases you enable today?
Hey @lawlm thanks! Companies and orgs in the finance space stand to benefit greatly from Openlayer. Many of the people we work with use us to help de-bias and improve the accuracy of models that predict whether to give loans, or whether a transaction is fraudulent, for example. These use cases are especially important because they have a large impact on people's lives, so it's critical to invest in evaluating these models.
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