DATA

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Enterprise Machine Learning Systems: MLOps Requirements and Abacus AI Solutions

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@kirkdborne

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09 January 2025 20h00

Building & Deploying Enterprise #MachineLearning Systems is hard. See the 30 requirements for an #MLOps environment. @AbacusAI handles all that for you. You bring the data and the use case. Abacus delivers the #ML environment: https://
abacus.ai/mlops #DataScience #AI #ML

→ View original post on X — @snorkelai

Building AI-Ready Data Management Without Perfect Data

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@snorkelai

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09 January 2025 18h56

New @Gartner_inc research insight: You don't need perfect data management to start your AI journey. Success comes from building AI-ready practices alongside your initiatives, not before them. Get the full roadmap and report here: https://
snorkel.ai/research/gartn
er-quick-answer-building-an-ai-ready-data-management-practice/?utm_campaign=resr-2024.11-gartneraidatamanage&utm_source=qualified&utm_medium=website&utm_content=header-offer
… #AI #DataStrategy

Synthetic Data for Pretraining: Proof-of-Concept Study

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09 January 2025 18h03

True, but I think it's more of a proof-of-concept / study of synthetic data for pretraining rather than generating a production-ready model.

Synthetic Data Structure Benefits and Knowledge Limitations

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09 January 2025 18h01

Yeah, I think the main advantage is from the more refined nature of the synthetic data when it comes to response structure etc. Synthetic data can't really contain knowledge that raw data doesn't already include because the raw data was used to come up with the synthetic data in

Synthetic Data Format Benefits Versus Raw Data Knowledge Quality

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09 January 2025 18h00

Yes. I think the main benefit is that it comes in a more structured or refined format compared to raw data. But the knowledge is the same as in the raw data (and may even be more hallucinated), considering that raw data was used to generate the synthetic data through a model.

→ View original post on X — @kirkdborne

Mastering RAG: Vector Databases and Prompt Engineering for AI Professionals

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@kirkdborne

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09 January 2025 17h46

This book is designed for AI researchers, data scientists, software developers, and business analysts looking to: Enhance data retrieval and AI accuracy using Retrieval-Augmented Generation (RAG). Master practical techniques like vector databases and prompt engineering.

→ View original post on X — @kirkdborne

Generative AI and RAG: Enhance LLMs with Internal Data Integration

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@kirkdborne

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09 January 2025 17h38

Unlocking Data with #GenerativeAI and RAG — Enhance Generative #AI systems by integrating internal data with large language models using RAG: https://
amzn.to/3PtFHdv v/ @PacktPublishing —————
#GenAI #LLMs #MachineLearning #KnowledgeGraph #DataScience #DataScientist #ad

Synthetic Data from High-Quality Models as Transfer Learning Jumpstart

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09 January 2025 17h35

Yeah, I see it as some flavor of transfer learning (i.e., not starting from raw data). Synthetic data generated by a high-quality model (such as GPT-4o, which has already undergone extensive refinement) may serve as a kind of jumpstart to the model you are trying to train.

→ View original post on X — @alteryx

Alteryx CEO Envisions AI-Ready Data Prep Canvas

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@alteryx

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09 January 2025 17h01

CEO @apmacmillan envisions Alteryx as the canvas for AI data prep—empowering teams to get data AI-ready, drive insights, and solve big challenges. Learn more about Andy’s vision: https://
ow.ly/Jwkp50UAEaT #AI #DataAnalytics #Leadership @ericavidon @TechTargetNews