Gartner defines AI evaluation and observability platforms (AEOPs) as tools that help manage the challenges of nondeterminism and unpredictability in AI systems. AEOPs automate evaluations (“evals”) to benchmark AI outputs against quality expectations such as performance, fairness and accuracy. These tools create a positive feedback loop by feeding observability data (logs, metrics, traces) back to evals, which helps improve system reliability and alignment. AEOPs can be procured as a stand-alone solution or as part of broader AI application development platforms.
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering.