Enterprises need a wide range of platforms, tools, skills, and techniques to operationalize data and analytics models into successful production applications. DataOps pipeline processes, for example, continuously integrate, transform, and prepare data for deployment into analytics applications. MLOps pipelines, on the other hand, continuously build, train, serve, and optimize machine learning, deep learning, natural language processing, and other statistical models.
But the number of enterprises using a new technique — joining DataOps and MLOps pipelines together — is expected to explode very soon. That’s because the unification of those two processes naturally supports popular enterprise data modernization initiatives like data lakehouses, data meshes, and virtualized data fabrics.
Read this new TDWI Best Practices report to learn:
- How and why enterprises use standalone DataOps and MLOps pipelines
- The important traits DataOps and MLOps pipelines share that make the processes ripe for unification
- How actual enterprises have unified their DataOps and MLOps pipelines to improve the development, testing, deployment, and optimization of sophisticated applications like advanced analytics and artificial intelligence
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