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For enterprise organizations, getting machine learning (ML) models to production and scaling use cases in the business has been a significant challenge. It is estimated that only about 12% of ML models make it to production environments today. To tackle this challenge, we released Cloudera Machine Learning (CML) MLOps — the most comprehensive and secure production ML platform, built on a 100% open-source standard and fully integrated with Cloudera Data Platform. CML breaks the wall to production and enables end-to-end ML workflows at scale.

JOIN THIS WEBINAR TO:
  • Learn how CML’s MLOps functionality eliminates the model black box and drives secure, transparent ML workflows from data to experimentation to production at scale.

  • Experience CML’s robust and flexible model monitoring service for both technical metrics (latency, throughput, etc.) and the mathematical/functional monitoring — including first-class prediction tracking, metric stores, and Python SDK.

  • See how CML’s unique model cataloging and model lineage capabilities eliminate silos and lead to better, faster results.

Speakers


Senior Manager, Product Marketing MLOps

Santiago Giraldo

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Santiago leads product marketing for Cloudera’s production machine learning products. With over 10 years in the data science and analytics software industry, Santiago focuses on enabling businesses to solve complex challenges with novel data strategies and machine learning approaches.

Senior Product Manager, MLOps

Alex Breshears

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Alex heads up product management for Cloudera's production machine learning products that enable customers to run hundreds to thousands of models at scale while meeting enterprise needs. Alex has also held positions in engineering and solutions engineering at Cloudera. Prior to Cloudera, he worked to develop, implement, and maintain marketing and loyalty systems at Supervalu and Walmart.

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