Datatron
@datatron
Datatron provides an enterprise-grade MLOps & AI model governance platform (de-coupled ) w/ model catalog for all of your AI/ML production models
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Every official photo of the Swiss federal council since 2000 Starting with 2025:
#datascientists #mlengineers You can only solve something when you acknowledge that there is an issue. What's your #mlops maturity? Gauge the maturity of your #ai #ml program across Ideation, Team, Stack, Process, & Outcome in this informative infographic. lnkd.in/gPpcpD-X
@datatron is proud to announce our latest release "Datatron 3.0" is now on @ProductHunt - #JupyterHub, #Kubernetes, and more...Check it out and we'd kindly appreciate upvotes. 😉 👍producthunt.com/posts/datatron
Datatron President Victor Thu is quoted in Protocol article discussing how companies are deciding between running #ml models on-prem or in the cloud. A very insightful article... protocol.com/enterprise/ai-… #machinelearning #datascience #artificialintelligence #mlops
Free #mlops webinar starting now *** No Registration required ***. See the features that leading enterprises are demanding. #datascientists streamline workflows via #jupyterhub integration. JOIN US NOW! No sign-up required. Just click below! us02web.zoom.us/j/87430266376
*** Today *** is the day. Datatron is hosting our "Datatron 3.0" Product Release Webinar. Starts at 11 am PT. You won't want to miss it! And if you do, we'll email you the recording link. Register Now! #ml #machinelearning #ArtificialIntelligence datatron.com/learn/webinars…
Join us as tomorrow (Tues) 7/26 for our "Datatron 3.0" product release webinar and learn about our #jupyternotebook #jupyterHub integration, simplified #kubernetes mgmt. and more enterprise features. #ml #DataScience #MLOps #mlopsplatform # Register: datatron.com/learn/webinars…
#datascientist are you using #jupyternotebook #jupyterhub? Our new integration, part of our "Datatron 3.0" #datatron30 release, allows you to upload, share & deploy your #ml #machinelearning models directly from your #notebook. #mlops #pytorch #tenstorflow datatron.com/product/releas…
Are you a #datascientist working in #jupyterhub #jupyternotebook? Check out Datatron's new integration - upload, download, deploy and share #MachineLearning models all from within the Notebook interface with which you are already familiar! datatron.com/product/releas…
#mlops #platform Datatron is proud to announce our latest release "Datatron 3.0" #datatron30 which includes a #jupyterhub #jupyternotebook integration, simplified #kubernetes management for #machinelearning. Check out the release notes datatron.com/product/releas…
A9: Fail fast with AI. This is a different mindset that’s different from traditional software. You need to deploy your AI in production so that you can learn quickly and make the appropriate adjustments. All the trainings in the lab will do ... #eweekchat crowdchat.net/s/869yu
A8: The economy today is a good forcing function for enterprises to stop treating AI like a toy. In 3 to 5 years, enterprises who pivoted from building their own tools will advance much rapidly as they focus on delivering real results rather... #eweekchat crowdchat.net/s/169yd
A5: The biggest myth is that data scientists strongly believe their models are so unique that no commercial software can handle their unique properties. This is not the case, and it stems from a knowledge gap that exists between teams. #eweekchat crowdchat.net/s/869x1
And the other thing is, don't fall in love with super high accuracy with your models. Sometimes the actual business results between 80% accuracy and 90% accuracy is not material. It's better to just deploy them! #eweekchat crowdchat.net/s/369wr
A4: Start with the fundamental. What business problem are you trying to solve and why AI. Some business challenges may not require sophisticated AI models. #eweekchat crowdchat.net/s/569wh
A3: 1) The over-romanticization of free open-source tools to deploy AI/ML. It’s hampering enterprises from getting any real ROI. 2) The scarcity of talents and the difficulties of hiring the right talents. #eweekchat crowdchat.net/s/369vy
Yes, @JamesMaguire, there's definitely a sense of concern especially the lack of ROI with such a huge investment. So if a large company is struggling, the smaller ones will feel the pain even more acutely. #eweekchat crowdchat.net/s/169vn
A2: Companies’ comfort level with AI is still very early. I was recently talking to an enterprise that is known to have a massive AI team and have done a lot to incorporate AI in their business. Only to find out most of the AI models are stil... #eweekchat crowdchat.net/s/869vd
A1: We see that the market is moving more so into MLOps. What used to be known as model development, data gathering, monitoring, etc are beginning to adopt the same term. #eweekchat crowdchat.net/s/069v2
#MLOps #live #tweet starting soon. @datatron's @victorthu will be sharing tips to "Expand Your #ai Deployment" in #MachineLearning #datascience #DataScientists crowdchat.net/chat/c3BvdF9vY…
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