Your browser is out of date

Update your browser to view this website correctly. Update my browser now

×

Cloudera Cloudera

WATCH NOW

Applied Machine Learning Prototypes (AMPs) are ML projects ready to deploy with the click of a mouse. Available in a browsable catalog in Cloudera Machine Learning (CML), each AMP provides an end-to-end framework for building, deploying, and monitoring business-ready ML applications instantly.

But even after an ML model is ready for enterprise-wide deployment, the effort to develop and deliver apps that take advantage of that model can prove exhausting and time consuming.

In this upcoming webinar, we’ll show you how AMPs work with Streamlit to transform ML models into functional visualizations and AI web apps in minutes. Join us and learn how data scientists can close the gap between ML deployment and app delivery—so that business users across the organization can put beneficial ML capabilities to work sooner.
 

During this 1-hour webinar you’ll:
 

  • Learn how AMPs use Streamlit to make user-ready ML web apps in minutes
  • Discover ways your business can benefit from AI capabilities with one-click-deployed predictive use cases
  • Watch as CML and Streamlit bring a model to life in no time flat, during a demo of our structural time series AMP

 

Register today to join us, and set yourself up for success with automated business app delivery.

Speakers

Cloudera Fast Forward Labs

Chris Wallace

More

Chris leads applied machine learning research at Cloudera Fast Forward Labs, where he works on making breakthroughs in machine intelligence accessible and applicable in the real world. He likes building data products and cares deeply about making technology work for people, not vice versa.

Head of Developer Relations

Randy Zwitch

More

Randy Zwitch is Head of Developer Relations at Streamlit, with broad industry experience in big data and data science. Randy is also an open-source contributor in the R, Python and Julia programming language communities.

Your form submission has failed.

This may have been caused by one of the following:

  • Your request timed out
  • A plugin/browser extension blocked the submission. If you have an ad blocking plugin please disable it and close this message to reload the page.