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Drive hyper-personalized customer experiences with Amazon Personalize and generative AI

AWS Machine Learning Blog

Amazon Personalize has helped us achieve high levels of automation in content customization. You follow the same process of data ingestion, training, and creating a batch inference job as in the previous use case. Getting recommendations along with metadata makes it more convenient to provide additional context to LLMs.

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Data architecture strategy for data quality

IBM Journey to AI blog

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving.

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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning Blog

To easily provide users with a large repository of relevant results, the solution should provide an automated way of searching through trusted sources. With an understanding of the problem and solution, the subsequent sections dive into how to automate data sourcing through the crawling of architecture diagrams from credible sources.

Metadata 101
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How the UNDP Independent Evaluation Office is using AWS AI/ML services to enhance the use of evaluation to support progress toward the Sustainable Development Goals

AWS Machine Learning Blog

Data ingestion and extraction Evaluation reports are prepared and submitted by UNDP program units across the globe—there is no standard report layout template or format. The data ingestion and extraction component ingests and extracts content from these unstructured documents.

ML 69
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Operationalizing Large Language Models: How LLMOps can help your LLM-based applications succeed

deepsense.ai

Other steps include: data ingestion, validation and preprocessing, model deployment and versioning of model artifacts, live monitoring of large language models in a production environment, monitoring the quality of deployed models and potentially retraining them. Not the best combination, right?

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How Can The Adoption of a Data Platform Simplify Data Governance For An Organization?

Pickl AI

Relying on a credible Data Governance platform is paramount to seamlessly implementing Data Governance policies. These platforms are centralized and designed to manage data practices, facilitate collaboration among different stakeholders, and automate the Data Governance workflow.

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Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

Bulk Data Load Data migration to Snowflake can be a challenge. The solution provides Snowpipe for extended data loading; however, sometimes, it’s not the best option. There can be alternatives that expedite and automate data flows. Therefore, quick data ingestion for instant use can be challenging.