IBM Journey to AI blog

AUGUST 15, 2023

A common phrase you’ll hear around AI is that artificial intelligence is only as good as the data foundation that shapes it. Therefore, a well-built AI for business program must also have a good data governance framework. Building and training foundation models Creating foundations models starts with clean data.

Artificial Intelligence 93

Artificial Intelligence Artificial Intelligence AI AI 93

DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. This process ensures that the dataset is of high quality and suitable for machine learning.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

DagsHub

FEBRUARY 29, 2024

Data preparation involves multiple processes, such as setting up the overall data ecosystem, including a data lake and feature store, data acquisition and procurement as required, data annotation, data cleaning, data feature processing and data governance.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

DagsHub

OCTOBER 23, 2024

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. It allows users to extract data from documents, and then you can configure workflows to pass the data downstream to LLMs for further processing.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52