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Cutting Edge Data Analytical Tools
Published in Chong Ho Alex Yu, Data Mining and Exploration, 2022
IBM Cloud Pak for Data, which is part of IBM Watson Studio, collects, organizes, and analyzes data across cloud platforms using AI technology In many organizations, it is common for data to be scattered across different platforms and servers in a siloed fashion, resulting in redundancy and inefficiency. IBM Cloud Pak for Data enables users to find existing data and to integrate them together in a single interface, thus removing barriers to collaboration. Because it is cloud-based, it can run anywhere and anytime. Currently it supports IBM Cloud, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Besides data management, Cloud Pak also offers a suite of AI-based analytical tools. Figure 3.9 shows some examples of AI solutions that are available in Cloud Pak, such as AutoAI experiment, Visual recognition, natural language processing, and deep learning (IBM 2021).
ML Lifecycle Canvas: Designing Machine Learning-Empowered UX with Material Lifecycle Thinking
Published in Human–Computer Interaction, 2020
Zhibin Zhou, Lingyun Sun, Yuyang Zhang, Xuanhui Liu, Qing Gong
Current programming tools assist novice designers in creating ML prototypes with simple code. These tools can be categorized as MLaaS (machine learning as a service platform) (Ribeiro et al., 2016), such as IBM Watson; open-source toolkits like TensorFlow (Abadi et al., 2016) that require programming skills; and non-programming tools including Yale and Wekinator (Fiebrink & Cook, 2010). Additionally, hardware platforms like AIY (Google, 2019b) provide easy-to-use tools to assemble physical ML artifacts. Platforms such as Google’s autoML (Google, 2019a) or IBM’s autoAI (IBM, 2019) help engineers and designers to easily construct or train high-quality ML models according to their needs. These facilitate designers in prototyping through appropriate simplification of the abstract technical details and processes of ML. However, they do not really help designers with little technical background to understand the mechanisms of ML and ideate ML-empowered UX when faced with the unfamiliar growable ML technology.