How to find a misbehaving model

Monitoring machine learning models once they are deployed can make the difference between a creating competitive advantage with ML and suffering setbacks that erode trust with your users and customers. But measuring ML model quality in production environments requires a different perspective and toolbox than monitoring normal software applications. In this talk, I share some practical techniques for identifying decaying models, along with strategies for providing this protection at scale in large organizations.

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Tristan Spaulding, Senior Director of Product Management @ DataRobot

 

What is DataOps Unleashed?

DataOps Unleashed is the official DataOps community.

We came together on March 17 as the emergence of DataOps, CloudOps, AIOps, MLOps, and other professionals, who gathered virtually to share the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

Sessions included talks by DataOps professionals at leading organizations, detailing how they’re establishing data predictability, increasing reliability, and reducing costs.

Join us in Winter 2022 for the next DataOps Unleashed!

What is the cost to watch the virtual sessions?

DataOps Unleashed is always free and open for all to attend and view on-demand.

Join us for sessions on:

  • Data pipelines
  • Data orchestration
  • Metadata
  • Data quality
  • Data governance
  • Data science platforms
  • AIOps and MLOps
  • CloudOps
  • Migrations
  • Observability
  • Optimization
  • Operations

Who comes to DataOps Unleashed?

DataOps professionals and experts including data administrators, data architects, data engineers, data analysts, AI/ML professionals, and data technology leadership.

If you'd like to speak at DataOps Unleashed, send a note to astronaut@solutionmonday.com.

New to DataOps?

DataOps is a holistic approach to the creation, deployment, monitoring, management, and optimization of data-driven applications. It describes the culture and rules of engagement that allow data teams to deliver and maintain high-quality, on-time data products, often powered by AI and machine learning, in an agile and cost-effective way.

DataOps defines how data teams work and also affects data consumers and those whose work causes new data to be created and used within the organization. Their work enables the entire organization to access data efficiently for data-driven decision-making and for the creation and delivery of data-driven applications.

Organizations with well-developed DataOps strategies, governance, and processes can expedite the delivery of data-driven workflows and results faster and better than others.

 

Sign up below to join us in 2022 at the next DataOps Unleashed

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