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.

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Marktechpost

JUNE 16, 2024

The feature eliminates the need for data teams to manually manage maintenance operations, such as scheduling jobs, diagnosing failures, and managing infrastructure. Anker: The data engineering team at Anker reported a 2x improvement in query performance and 50% savings in storage costs after enabling Predictive Optimization.

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AWS Machine Learning Blog

OCTOBER 2, 2024

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.

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Marktechpost

DECEMBER 3, 2024

Whether users need data from structured Excel spreadsheets or more unstructured formats like PowerPoint presentations, MegaParse provides efficient parsing while maintaining data integrity. The significance of MegaParse lies not just in its versatility but also in its focus on information integrity and efficiency.

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Unite.AI

JUNE 4, 2024

Google Cloud’s AI and machine learning services, including the new generative AI models, empower businesses to harness advanced analytics, automate complex processes, and enhance customer experiences. Next, we focused on enhancing their data ingestion and validation processes.

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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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