Data Ingestion Data Platform Generative AI 
IBM Journey to AI blog
JUNE 27, 2024
Generative AI has altered the tech industry by introducing new data risks, such as sensitive data leakage through large language models (LLMs), and driving an increase in requirements from regulatory bodies and governments.
AWS Machine Learning Blog
JUNE 18, 2024
This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. A human-in-the-loop mechanism safeguards data ingestion.
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AWS Machine Learning Blog
FEBRUARY 21, 2025
Rockets legacy data science architecture is shown in the following diagram. The diagram depicts the flow; the key components are detailed below: Data Ingestion: Data is ingested into the system using Attunity data ingestion in Spark SQL.
IBM Journey to AI blog
SEPTEMBER 20, 2023
Large language models (LLMs) have taken the field of AI by storm. Scale and accelerate the impact of AI There are several steps to building and deploying a foundational model (FM). brings new generative AI capabilities—powered by FMs and traditional machine learning (ML)—into a powerful studio spanning the AI lifecycle.
Unite.AI
JUNE 4, 2024
Lastly, the integration of generative AI is set to revolutionize business operations across various industries. 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.
AWS Machine Learning Blog
NOVEMBER 15, 2023
The teams built a new data ingestion mechanism, allowing the CTR files to be jointly delivered with the audio file to an S3 bucket. In the future, Principal plans to continue expanding postprocessing capabilities with additional data aggregation, analytics, and natural language generation (NLG) models for text summarization.
AWS Machine Learning Blog
FEBRUARY 27, 2024
Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.
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