ETL Metadata Prompt Engineer 
AWS Machine Learning Blog
JANUARY 7, 2025
The solution proposed in this post relies on LLMs context learning capabilities and prompt engineering. When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata. The request is sent to the prompt generator. You should see a noticeable increase in the quality score.
AWS Machine Learning Blog
JUNE 25, 2024
Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced. Prerequisites This post is intended for developers with a basic understanding of LLM and prompt engineering. A prompt is natural language text describing the task that an AI should perform.
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IBM Journey to AI blog
JULY 17, 2023
offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting. These Slate models are fine-tuned via Jupyter notebooks and APIs. To bridge the tuning gap, watsonx.ai
AWS Machine Learning Blog
APRIL 16, 2024
You can use these connections for both source and target data, and even reuse the same connection across multiple crawlers or extract, transform, and load (ETL) jobs. Text to SQL: Using natural language to enhance query authoring SQL is a complex language that requires an understanding of databases, tables, syntaxes, and metadata.
The MLOps Blog
AUGUST 3, 2023
At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. We have someone from Adobe using it to help manage some prompt engineering work that they’re doing, for example.
AWS Machine Learning Blog
SEPTEMBER 1, 2023
These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.
NOVEMBER 24, 2023
By analyzing millions of metadata elements and data flows, Iris could make intelligent suggestions to users, democratizing data integration and allowing even those without a deep technical background to create complex workflows. The example in the following prompt shows a fictitious schema that matches the expected output.
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