Deepchecks Monitoring takes the open source testing experience all the way to production: enabling you to send data over time, explore system status and receive alerts on problems that arise over time.
Hi Product Hunt! 🚀
I'm Shir Chorev, Co-founder and CTO of Deepchecks, and I’m thrilled to introduce you to our open-source monitoring solution for AI systems in production! When we launched our open-source testing package last year, we quickly received an overwhelming response with over 2.5K stars ⭐️ and more than 600K downloads! The success of our approach has motivated us to level up the Deepchecks experience for the AI ecosystem.
We originally intended for our monitoring component to be closed, with a focus on companies already using Deepchecks testing. However, we quickly discovered that there were significant needs in the community for open-source monitoring. Here's why we decided to open source it:
📊 An open-source package enables teams of any size or budget to access state-of-the-art ML monitoring features, with a customizable stay-for-free-forever solution.
🔒 Users can try before committing, without sharing sensitive data with any third parties.
💪 It empowers creative, proactive individuals to quickly ideate and prototype with a powerful solution over a matter of days.
So, here it is, our open-source monitoring solution! It offers a comprehensive set of features, including:
➡️ Root cause analysis capabilities that integrate seamlessly with our UI and Jupyter notebooks
➡️ Tracking checks results over time and setting alert rules to be triggered upon certain conditions
➡️ Monitoring of one model per deployment & basic user management
As an open-core strategy, we also offer premium features catering to advanced teams, including advanced security, scalable deployment options, a centralized dashboard, and audit/compliance testing templates. By keeping our commitment to the open-source community, we strive to build a robust ecosystem of tools for “Continuous Validation” of AI pipelines from research to production.
It’s never been easier or more accessible to monitor the health of your models & data.
Star us ⭐️ on Github: https://www.github.com/deepchecks/deepchecks
Useful links to dive right in:
• Get Started with Deepchecks Monitoring
• Join Our Slack Community for more updates and info about ML validation and monitoring
I'm not using their product but witnessed their passion for data quality and knowledge in this domain that is crucial to any data professional. Good luck
Really impressive idea! Would it be okay to include your app in my directory? (producthunt.com/products/wikiaitools). Email us with your product details and we'll also help you get your app listed on 100+ AI directories...
"Shir, I'm thoroughly impressed by your idea. It's a game-changer that has the potential to redefine industry standards."
Congrats! Really like your approach of democratizing our AI world - we need more products like this! Beside, super convenient to use.
Deepchecks is one of the coolest MLOps tools on the market. Period. I've struggled a lot with monitoring, and this tool hits all the major pain points. Kudos!
That is a good problem to solve, many ML systems lack proper monitoring. Good luck with the launch!
Congrats on the launch. Strengthening the open source ecosystem around ML and AI is great balance to all the commercial closed source movements in the field. CI/CD and monitoring also really needed in this field. Good luck!
Congratulations on the launch. I am building a chatbot SAAS leveraging OpenAI. This seems exciting to me for scale and availability.
Absolutely amazing! 🤩 I'll be happy to join the crew of users, I love Deepchecks features! Congratulations on the launch to the team! 🤗🥳💪✨
Congrats! Exciting!
Please share more details about the data processing: Is it done locally or on a remote server?
So, I am asking about data privacy if to be direct.
I loved other Deepchecks tools, so I can't wait to try this one 🚀
