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March 11, 2024
Cross-cloud MLOps with Tanzu Application Platform and VMware Data Solutions: A 4-Minute Primer
With Tanzu Application Platform and VMware Data Solutions, ML engineers can implement end-to-end machine learning operations in a cloud-agnostic manner, using opensource-first tooling, industry-leading data products, and a GitOps-ready foundation.
MLOps: Streamlining the path to production-ready machine learning
Today, the world of AI must grapple with a strange paradox. Thanks to the emergence of state-of-the-art model architectures, ultra-large-scale data, and recent advances in compute, storage and networking, the impact of AI and machine learning on modern industry has been revolutionary. Yet, the operational process behind AI itself continues to lag significantly behind. Surveys show that up to 90% of ML models fail to make it to production, and those that do take up to three months to deploy (on average). Moving from pilot to production remains a formidable challenge for many enterprises.
MLOps was designed to try and address this problem. Using mechanisms like automation (complete and partial), continuous improvement, continuous monitoring and shared collaboration, it provides a framework for managing and deploying machine learning models with greater efficiency, agility and security. If this sounds familiar, it’s because it is: MLOps is actually an offshoot of DevOps. Back when DevOps was first conceived in the 2000s, enterprise developers were having similar struggles with releasing their application workloads to production at scale. Now, high-velocity, agile software development teams are much more commonplace. In turn, MLOps aims to reproduce DevOps successes from the software development world by incorporating the same foundational principles, while also managing ML-specific concerns like drift, governance, trust and continuous training.