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 ā€œorganizations 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.ā€

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.Ā  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.Ā  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 ā€œat the core of an organization’s AI strategyā€ and is ā€œfocused on operationalizing AI models, including the full life cycle management of AI decision models and AI governance.ā€Ā  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

DataOpsĀ 

DataOps provides ā€œthe foundational data operations for operationalizing AI models.Ā  It improves the flow of data to points of consumption in the business.ā€Ā  DataOps describes ā€œhow you do data management.ā€

On-Demand Webinar: Why Do DataOps?

Book: The DataOps Cookbook

White Paper: 7 Steps to Implement DataOps

Data-AI Role Alignment

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

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

White Paper:Ā  Reducing Organizational Complexity with DataOps

Blog: Improving Teamwork in Data Analytics with DataOps

Product Development Focus for AI Agility

ā€œIntroducing product deployment practices to data management is essential.ā€ 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.Ā Ā 

 

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