Francisco Saavedra’s Post

What is in-database machine learning? And how does it help organizations code, build, test, and deploy machine learning models within database environments? Find out: https://lnkd.in/eK4a5tcG

What is in-database machine learning? And how does it help organizations code, build, test, and deploy machine learning models within database environments? Find out: https://lnkd.in/gZShMN3C

What is in-database machine learning? And how does it help organizations code, build, test, and deploy machine learning models within database environments? Find out: https://lnkd.in/dmCMBTJz

What is in-database machine learning? And how does it help organizations code, build, test, and deploy machine learning models within database environments? Find out: https://lnkd.in/gfyTRwfs

What is in-database machine learning? And how does it help organizations code, build, test, and deploy machine learning models within database environments? Find out: https://lnkd.in/dJ9GdhUT

What is in-database machine learning? And how does it help organizations code, build, test, and deploy machine learning models within database environments? Find out: https://lnkd.in/dMjPkEvk

Machine learning workflows often require efficient data storage and retrieval mechanisms, especially when dealing with high-dimensional data. Vector databases provide an ideal solution for these needs. #machinelearning #vector #database #datastorage #software https://lnkd.in/gnPgjrGt

An AI integration for Azure SQL databases. Retrieval Augmented Generation is an incredible bridge for structured data with generative AI to enhance natural language and queries across applications. Awesome feature #Microsoft #SQL #AI #LLM #COPILOT

I believe AI having a greater role in being 'behind the scenes'! Improving productivity, efficiency, accessibility and user experience; even without users noticing it explicitly --baked nicely into the users' natural eco-system of tools. And this is where I see IBM's Text-to-SQL bridging the gap between complex database systems and users; making data more accessible and usable; especially in the space of Handling Large Queries --creating complex queries that might be challenging to construct manually. How do you see AI shaping the future Tech work-force? Full blog here:

"In the age of big data, extracting meaningful insights from vast datasets is a daunting challenge. In a recent video, Martin Keen, a Master Inventor at IBM, delves into Principal Component Analysis (PCA) as a powerful tool for simplifying complex data. Keen’s discussion offers a detailed exploration of PCA, highlighting its applications in various fields such as finance and healthcare and underscoring its significance in machine learning." Learn more by reading Rich Ord's article in WebProNews! #developers #machinelearning #IBM https://lnkd.in/g95bGMrs