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On Demand

Enterprises are increasingly testing, iterating, and deploying more machine learning models across business processes and products. As success is realized and scale increases, managing ML models becomes progressively more challenging. So, how can you manage operational workflows in a standardized process across the organization? One solution is to establish universal software and process standards for managing ML operations.  These standards should normalize and streamline how ML engineers, operators, and managers monitor and govern models at scale.

In this webinar, you’ll learn how to: 
  • Overcome common challenges with managing production ML workflows

  • Implement universal enterprise-grade standards for creating, monitoring and governing ML models 

  • Adopt and shape these standards to fit your organization’s needs and ML workflows

This webinar is essential for any stakeholder grappling with how to manage, operationalize and scale machine learning across their business.



Product Manager: ML & Data Engineering

Alex Breshears


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.

Product Marketing Manager, Data Engineering

Santiago Giraldo Anduaga


Santiago is responsible for driving the marketing and strategy initiatives for data engineering and machine learning operations at Cloudera. Prior to joining Cloudera, Santiago developed and led global technical evangelism and go to market strategies for companies in the data analytics, machine learning, and spatial data science spaces.

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