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Machine Learning systems can be categorized in eight different categories: data collection, data processing, feature engineering, data labeling, model design, model training and optimization, endpoint deployment, and endpoint monitoring. Each step in the machine learning lifecycle is built in its own system, but requires interconnection.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Microsoft Azure DevOps, Jira, Requirements.cc, Excel, Word Provides management of actors, use cases, user stories, declarative requirements, and test scenarios. Includes glossary, data dictionary, and issue tracking. Supports use case diagrams, auto-generated flow diagrams, screen mock-ups, and free-form diagrams. clang-uml: Unknown Unknown
When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text , a collection of images, sensor data, and data collected from individual users of a service.
Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. [1] It can be used to deploy ML models on embedded systems and edge-devices. [2] [3] The platform was launched in November 2017. [4]
ModelOps (model operations or model operationalization), as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models" in Multi-Agent Systems. [1] "
A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are common examples of foundation models.
UML Diagrams used to represent the development view include the Package diagram and the Component diagram. [2] Physical view: The physical view (aka the deployment view) depicts the system from a system engineer's point of view. It is concerned with the topology of software components on the physical layer as well as the physical connections ...