DATA

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Eight-Step AI Process: From Problem Definition to Responsible Deployment

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@ingliguori

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25 April 2026 19h25

AI isn’t magic. It’s a process. 8 steps:
define problem
collect/prepare data
choose model
train
evaluate
fine-tune
deploy
ensure ethics & safety Real value comes from running this loop well. #AI #MachineLearning #DataScience #ResponsibleAI

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Lakebase Enables Fast Autoscaling OLTP Database for Agentic Workflows

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@databricks

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25 April 2026 16h29

With Lakebase, you can spin up an autoscaling OLTP database, connect to it, and start writing data in seconds.@MrSiWhiteley shows how quickly you can create a database, add data, and start using it — making it a strong fit for applications and agentic workflows that need fast,… pic.twitter.com/dSF0XT9sWk
— Databricks (@databricks) 25 avril 2026

With Lakebase, you can spin up an autoscaling OLTP database, connect to it, and start writing data in seconds. @MrSiWhiteley shows how quickly you can create a database, add data, and start using it — making it a strong fit for applications and agentic workflows that need fast,

Data Governance: Breaking AI Pilot Mode Deadlock

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25 April 2026 15h49

Will your AI initiatives still be stuck in pilot mode next year, or will you solve the data governance challenge first?

Data Preparation Discipline Separates AI Experimenters from Producers

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25 April 2026 15h27

The next 12 months will separate AI experimenters from AI producers. The difference won't be model choice – it'll be data preparation discipline.

Data governance gap widens between leading and lagging companies

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25 April 2026 15h15

We're about to see a split between companies that crack data governance and those that keep throwing compute power at garbage data. The gap will be massive.

Document Governance: The Real AI ROI Determinant

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25 April 2026 15h10

AI projects will stay stuck in experimental purgatory until companies solve the unstructured data problem. The future belongs to organizations that master document governance, not just model sophistication. Data discipline determines AI ROI.Partner content with Adlib. #adlib_iiot pic.twitter.com/lKEz0FGKaX
— Lucian Fogoros (@fogoros) 25 avril 2026

AI projects will stay stuck in experimental purgatory until companies solve the unstructured data problem. The future belongs to organizations that master document governance, not just model sophistication. Data discipline determines AI ROI.Partner content with Adlib. #adlib_iiot

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ML Workflow: Data Understanding to Deployment and Monitoring

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@ingliguori

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25 April 2026 14h17

ML isn’t magic — it’s a workflow. 1. understand data 2. choose right algorithm 3. train 4. test 5. optimize 6. deploy + monitor + retrain The winners are the teams who run this loop consistently. #MachineLearning #AI #DataScience #MLops