IBM Journey to AI blog

JANUARY 9, 2024

In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models. A popular method is extract, load, transform (ELT).

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IBM Journey to AI blog

JANUARY 5, 2023

Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving. Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads.

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Pickl AI

APRIL 14, 2023

Data Processes and Organizational Structure Data Governance access controls enable the end-users to see how data processing works inside an organization. It can include data refresh cadences, PII limitations, regulatory data regulations, or even data access. It ensures the safe storage of data.

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Unite.AI

DECEMBER 5, 2023

ETL ( Extract, Transform, Load ) Pipeline: It is a data integration mechanism responsible for extracting data from data sources, transforming it into a suitable format, and loading it into the data destination like a data warehouse. The pipeline ensures correct, complete, and consistent data.

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Pickl AI

OCTOBER 19, 2023

This includes removing duplicates, correcting typos, and standardizing data formats. It forms the bedrock of data quality improvement. Implement Data Validation Rules To maintain data integrity, establish strict validation rules. This ensures that the data entered meets predefined criteria.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

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The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

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