Gartner: Operational AI Requires Data Engineering, DataOps, and Data-AI Role Alignment

Recommendations for Further Reading

In Gartner’s recent report, Operational AI Requires Data Engineering, DataOps, and Data-AI Role Alignment, Robert Thanaraj and Erick Brethenoux recognize that “organizations are not familiar with the processes needed to scale and promote artificial intelligence models from the prototype to the production stages; resulting in uncoordinated production deployment attempts.”

In fact, only 1 in 10 organizations are able to get 75% or more of their AI prototypes into production and it takes 9 months on average to do so.  This is similar to findings in a joint Eckerson-DataKitchen DataOps survey.

In this report, Gartner outlines recommendations to effectively operationalize AI solutions that involve the core management competencies of ModelOps, DataOps, and DevOps.  Although there is a good degree of overlap between these practices, Figure 1 illustrates their interrelationship.

Figure 1: Operational AI Requires ModelOps, DataOps, and DevOps Practices

 

Below we provide additional suggestions for further reading based on Gartner’s recommendations.

ModelOps

ModelOps is “at the core of an organization’s AI strategy” and is “focused on operationalizing AI models, including the full life cycle management of AI decision models and AI governance.”  ModelOps depends on a comprehensive data foundation enabled by data engineering practices and DataOps.

Blog: Deliver AI and ML Models at Scale with ModelOps

On-Demand Webinar: Your Model is Not an Island: Operationalize Machine Learning at Scale with ModelOps

White Paper: Governance as Code

DataOps 

DataOps provides “the foundational data operations for operationalizing AI models.  It improves the flow of data to points of consumption in the business.”  DataOps describes “how you do data management.”

On-Demand Webinar: Why Do DataOps?

Book: The DataOps Cookbook

White Paper: 7 Steps to Implement DataOps

Data-AI Role Alignment

DataOps is a “collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers.”  Therefore, “data and analytics leaders should set up cross-functional data and AI teams with both traditional and modern roles.”

On-Demand Webinar: How to Build a Winning Data Team

White Paper:  Reducing Organizational Complexity with DataOps

Blog: Improving Teamwork in Data Analytics with DataOps

Product Development Focus for AI Agility

“Introducing product deployment practices to data management is essential.” These practices include CI/CD automation and automated testing.

Blog: Add DataOps Tests for Error-Free Analytics

Blog: Add DataOps Tests to Deploy with Confidence

White Paper: DataOps is Not Just DevOps for Data

On-Demand Webinar – Orchestrate Development Pipelines for Fast and Fearless Deployment

For more information on operationalizing AI models, you can read the entire Gartner report here.  

 

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