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 AI platforms for data science and machine learning (DSML) as platforms that support end-to-end AI model and agent development and life cycle management using diverse data science and AI techniques. These platforms enable data preparation, model building, deployment and governance, and are delivered as fully managed cloud services or on-premises infrastructure for AI experts and business users. AI platforms for DSML primarily target AI experts, including data scientists and AI engineers, providing a comprehensive suite of data science and AI techniques for augmented and automated decision making. These platforms support building models using data-intensive methods such as machine learning, as well as techniques such as simulation and optimization. They handle all types of data — tabular, image, video and text — for multimodal applications, such as computer vision, natural language processing (NLP) or composite AI, that combines optimization with machine learning.