Wie entwirft man Datenmodelle für maschinelles Lernen und KI?

El modelado de datos es fundamental en diversas disciplinas, incluyendo bases de datos y análisis. Algunos conceptos básicos: Las entidades son objetos o conceptos identificables (como una persona o un producto), y los atributos son las características que describen esas entidades (por ejemplo, nombre, edad o precio). Indican cómo las entidades se relacionan entre sí. Por ejemplo, una relación podría existir entre una entidad "Estudiante" y otra "Curso". Un modelo gráfico que representa entidades, atributos y relaciones. Ayuda a visualizar la estructura de la base de datos. Estos conceptos son esenciales para comprender y crear estructuras de datos efectivas y eficientes.

Designing data models for machine learning and artificial intelligence involves defining the problem, gathering a diverse dataset, understanding the data, data preprocessing, feature engineering, handling imbalanced data, temporal considerations, splitting the dataset, normalizing and standardizing numerical features, choosing the appropriate ML model, hyperparameter tuning, evaluating model performance, iterative model refinement, handling bias and fairness, model interpretability, deployment and monitoring, data governance and compliance, documentation, collaboration across disciplines, and staying informed and adaptive.

• A conceptual data model is a high-level data model that provides an overview of the main entities and relationships within a system, without delving into the details of data attributes, keys, constraints or implementation specifics. On the other hand, a logical data model is a more detailed data model that outlines the data attributes, keys, constraints and relationships within the system using a standard notation, such as an entity-relationship (ER) model or unified modeling language (UML) class diagram. Finally, a physical data model is a specific data model that provides a detailed description of the system's data structures, indexes, views, triggers, and other physical aspects, using a specific data model language such as SQL or XML

In data modeling for AI, the focus is on preparing data to effectively train machine learning models. This involves identifying relevant data sources and features that are linked to the problem. Domain expertise aids in understanding these relationships. Preprocessing is key, including cleaning, normalizing, and encoding data to suit algorithmic needs. The problem type dictates the modeling approach, with different strategies for classification, regression, or time-series data. Model selection and training method (like neural networks for complex patterns) are tailored to the data's nature. This process is iterative, requiring continuous refinement based on model performance, blending scientific precision with creative problem-solving.

As long as your data is governed and modelled correctly, any data model can be used. As long as its uniform, consistent and appropriate for what u want to do. Its not so much as about which model to use, but rather is it implemented correctly. As always there will be exceptions on which is an appropriate data model or methodology to use. But these exceptions are far and few, and generally highly complex and specialised. But for 99% of all company’s most mainstream data model will suffice, as long as its implemented correctly and governed appropriately.

En el contexto del aprendizaje automático (AA) y la inteligencia artificial (IA), el modelado de datos implica preparar los datos de entrada de manera adecuada para entrenar modelos. El modelado de datos para AA y IA es un paso crucial que influye directamente en el rendimiento y la generalización de los modelos. La comprensión profunda del dominio y de las características específicas del conjunto de datos es esencial.

There are many data modeling techniques for machine learning and AI. • Data preprocessing: This involves cleaning, filtering, normalizing, and standardizing the data to remove noise, outliers, missing values, and inconsistencies. • Data integration: This involves combining, merging, and aligning data from different sources, formats, and scales to create a unified and consistent data set. • Data transformation: This involves applying mathematical, statistical, or logical operations and functions to the data to create new features, varia • Data visualization: This involves presenting, displaying, or plotting the data in graphical or interactive forms to explore, analyze, or communicate its characteristics, patterns, or trends.

With a number of AI thats deal in realtime, such as specilized chat bots, and AI used for fraud detection as a few examples. The biggest issue with modelling in the context of AI. Is real time modelling. When u are training a model, it is vital u keep feeding it data, in a contiguous fashion. If there are significant time breaks in the training data ingestion into the model. You increase the propensity, to inject biasis’s. U will also loose context of the data. Which is important for accuracy. So when choosing a data model, its important to be able to model the data in as near real time as possible.

El modelado de datos presenta varios desafíos que pueden afectar la efectividad y el rendimiento de los modelos en aprendizaje automático e inteligencia artificial. Superar estos desafíos requiere un enfoque cuidadoso, comprensión profunda del dominio y la aplicación de prácticas sólidas de ciencia de datos.

• Handling data variety and heterogeneity: Data can come from various sources, Data modelling needs to handle the data variety and heterogeneity by ensuring the data quality, consistency, and compatibility across different sources and formats. • Managing data evolution and change: Data is not static and it can change over time due to various reasons. Data modelling needs to manage the data evolution and change by maintaining the data history, versioning, and lineage, as well as the data migration, transformation, and synchronization processes. • Dealing with data complexity and uncertainty: Data can be complex and uncertain due to various factors, such as the presence of missing, incomplete, inaccurate, or inconsistent data.

In developing the Clymbr AI-EdTech Platform, we’ve crafted data modules to enhance machine learning capabilities. Initial stages involved defining learning objectives and understanding diverse data sources. We applied extensive data preprocessing techniques, handling variations in student input and content. Feature engineering played a vital role, tailoring inputs to optimise AI models for adaptive learning. We leveraged version control to track dataset modifications, ensuring transparency and reproducibility in training. Iterative updates based on user interactions and educational outcomes allowed us to refine the data modules continually. This approach ensures Clymbr remains adaptive, while maintaining standards in data quality

How to build a model which can scale, be relevant for the future and also not an over kill is a very big challenge in addition to building it within an ethical and legal framework. For instance: if a data model is built for Medical data for exploration of possibility of a disease using some demographic, clinical data. We need to keep in mind 1. Elimination of similar or coorulated metrics (BP and Salt content in Blood) 2. Ethical pointers on not having personally identifiable data of the patients but having demographic information.

Algunas prácticas recomendadas de modelado de datos en el contexto del aprendizaje automático y la inteligencia artificial incluyen: Realizar un análisis exploratorio de datos para comprender la distribución, la variabilidad y las relaciones entre las variables antes de iniciar el modelado. Identificar y abordar valores atípicos, datos faltantes y errores para garantizar la calidad de los datos de entrada. Estas prácticas y muchas otras más contribuyen a la construcción de modelos más sólidos, interpretables y éticos. La iteración continua y el aprendizaje de los resultados son esenciales para mejorar y adaptar los modelos a medida que se enfrentan a nuevos desafíos.

• Identify the data sources and the data users of the system. • Define the data requirements and the data scope of the system. • Create a conceptual data model that represents the main entities and relationships in the system. • Validate the conceptual data model with the stakeholders and the users. • Create a logical data model that specifies the data attributes, keys, constraints, and relationships in the system. • Validate the logical data model with the developers and the analysts. • Create a physical data model that defines the data structures, indexes, views, triggers, and other physical aspects of the system. • Implement the physical data model in the database or the data warehouse. • Test and refine the data model as needed.

Any model built for supervised or unsupervised learning without looking at consistency, skew, missing attributes, errors would give a poor result. It is important to choose a model compatible as well with the nature of output and underlying data

1. Modelo de Datos Relacional:** Una base de datos de una biblioteca podría tener tablas para libros, autores y préstamos, con relaciones entre ellas 2. Modelo de Datos de Red: En una red social, los nodos podrían representar usuarios, y las relaciones podrían ser amistades entre ellos 3. Modelo Dimensional: En un almacén de datos de ventas, los hechos podrían ser las ventas diarias, y las dimensiones podrían incluir tiempo, producto y ubicación 4. Modelo de Datos Semánticos: En un entorno médico, podría modelar la relación entre enfermedades, síntomas y tratamientos de manera semántica 5.Modelo de Datos de Grafos: En logística, podrías representar rutas de entrega como nodos y las conexiones entre ellos como relaciones en un grafo

• Conceptual data model: A high-level data model that describes the main entities and relationships in the system, without specifying the details of the data attributes, keys, constraints, or implementation. • Logical data model: A detailed data model that describes the data attributes, keys, constraints, and relationships in the system, using a standard notation, such as entity-relationship (ER) model or unified modelling language (UML) class diagram. • Physical data model: A specific data model that describes the data structures, indexes, views, triggers, and other physical aspects of the system, using a specific data model language, such as SQL or XML. It is used to implement the data design and the data operations of the system.

Data engineering capabilities that enable multiple data sources and structures that your features require is critical. Activation of these pipelines to meet “real-time” requirements is the optimal goal. With “real-time” defined in the context of decision science, and not simply for the sake of real-time. These requirements have influence evolution in the data virtualisation and fabric architectures. Edge computing developments will further contribute to optimisation of performance of data engineering capabilities that designs will leverage in the future.

We have run models for stock market data in supervised learning to find possible sudden sparks in prices, what is important is keep this model Alive by constantly feeding it data , which can challenge its initial findings or reiterate the original findings but a model which is not live and long would lose its purpose