Machine learning developers face an expanded set of management issues beyond merely getting the code right, including the testing and validation of data used in ML models while handling an additional set of infrastructure dependencies. After deployment, those models will degrade over time as use cases evolve. In response to growing calls for standardization of machine learning operations, cloud and tool vendors are promoting new services aimed at making life a bit easier for data scientists and machine learning developers. Among them is Google Cloud, which this week dropped a batch of cloud AI tools that include data pipelines, metadata and a "prediction backend" for automating steps in the MLOps workflow. "Creating an ML model is the easy part--operationalizing and managing the lifecycle of ML models, data and experiments is where it gets complicated," Craig Wiley, director of product management for Google's cloud AI platform, noted in a blog post unveiling the MLOps services. The "MLOps foundation" is perhaps the most compelling of the cloud AI tools unveiled this week by the public cloud and AutoML vendor (NASDAQ: GOOGL).

Machine learning developers face an expanded set of management issues beyond merely getting the code right, including the testing and validation …