Gartner defines cloud AI developer services (CAIDS) as cloud-hosted or containerized services and products that enable software developers who are not data science experts to use artificial intelligence (AI) models via APIs, software development kits (SDKs) or applications. Core capabilities include automated machine learning (autoML) including automated data preparation, automated feature engineering and automated model building, and model management and operationalization for language, vision and tabular use cases. Optional and important complementary capabilities include AI code models and assistants. Cloud AI developer services help organizations embed intelligence, such as AI and ML insights, into their applications. While that is what cloud AI developer services offer, it is more important to note how they accomplish this. These services democratize and increase the availability of AI and ML to software engineers through the automation and features offered. Traditional activities regarding data acquisition, data quality, feature engineering, algorithm selection and model training are augmented by the technology. Cloud AI developer services open up a world of possibilities for software engineers to build AI and ML production capabilities and features for enterprise-built applications.
Emotion AI, also known as Affective AI, refers to the area of artificial intelligence where systems are designed to recognize, interpret, process and simulate human emotions. The goal is to allow machines to understand and respond to human emotions in a way that feels more intuitive and human-like. The techniques used in Emotion AI include facial expression analysis, voice tone analysis, physiological measurement (e.g., heart rate variability), and natural language processing. Integration with other systems like CRM and virtual assistants also facilitates emotionally-aware interactions. Using these various techniques to analyze emotions in real time has spawned new use cases for customer experience enhancements, employee wellness and many other areas, including entertainment, healthcare, automotive, retail and advertising and education.
Visual intelligence helps customers find products with relevant visual attributes using real-world images, video and text. Expanding on visual search capabilities, this helps customers identify a specific product, provides related content or detailed information, or otherwise triggers engagement. These solutions analyze catalogs to understand taxonomy and product attributes in addition to visual features, product identification using technologies like computer vision, natural language processing and machine learning. This technology helps ease the path to purchase, driving consumers in sectors like Healthcare, Sports & entertainment, manufacturing , retail etc., from awareness to conversion in an instant by presenting products with relevant visual attributes.