Developing an AI model is only the first step in an AI DevOps process. Gartner estimates that “by 2022, at least 50% of machine learning projects will not be fully deployed in production.”* To reap the business benefits from your AI investment, the model must be operationalized into a production deployment. Deploying to an operational system is more than just providing a RESTful API endpoint.
In this Gartner report, you will discover:
- Best practices for AI DevOps, which incorporates MLOps and DataOps
- Key challenges technology leaders face in moving AI and machine learning models to production
- How and where DevOps helps shorten the time and reduce the risk of operationalizing models in production
- The importance of DataOps culture
You can deploy your MATLAB® AI models to vehicles, industrial equipment, operational systems, enterprise applications, or browser-based applications without recoding in another language.
* Gartner, Accelerate Your Machine Learning and Artificial Intelligence Journey Using These DevOps Best Practices, Arun Chandrasekaran, Farhan Choudhary, 12 November 2019.