Remove Data Modeling Remove Data Preparation Remove Data Quality
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Looking Ahead: The Future of Data Preparation for Generative AI

Data Science Blog

Businesses need to understand the trends in data preparation to adapt and succeed. If you input poor-quality data into an AI system, the results will be poor. This principle highlights the need for careful data preparation, ensuring that the input data is accurate, consistent, and relevant.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

professionals

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What is a data fabric?

Tableau

We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage. Data modeling.

Tableau 101
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What is a data fabric?

Tableau

We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage. Data modeling.

Tableau 98
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Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning Blog

Unified model governance architecture ML governance enforces the ethical, legal, and efficient use of ML systems by addressing concerns like bias, transparency, explainability, and accountability. Associate the model to the ML project and record qualitative information about the model, such as purpose, assumptions, and owner.

ML 100
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What is Data-Centric Architecture in AI?

Pickl AI

Data Collection The process begins with the collection of relevant and diverse data from various sources. This can include structured data (e.g., databases, spreadsheets) as well as unstructured data (e.g., Data Preparation Once collected, the data needs to be preprocessed and prepared for analysis.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Data monitoring tools help monitor the quality of the data.