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Data, analytics and machine learning are the foundation for AI (artificial intelligence). The challenge with data is the variety across locations (cloud, on-prem, private cloud), types (structured, unstructured), and platforms (operational database, data warehouse, hadoop, fast data platforms, etc). Once we deal with our data management, we are able to move on to analytics, which lets us extract insight from our data. Predictive analytics leads us to machine learning. Once we develop enough machine learning models, we are beginning to reach AI.

IBM’s solution to managing big data is the Hybrid Data Management Platform. Check it out:

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Sami Sabir says:

Excellent. i finally understand.

iman hdairis says:

Hi Caleb! Thank you for the video, it was very informative. Is there any way i could get in touch with you? I would like to ask for more details. Appreciate any help i could get 🙂

walito gama says:

it is wonderful information.

Chaparro says:

you are chobbier. Well made video

Thomas Henson says:

Wait Machine Learning isn't a magic box? Great video. One way I talk about Machine Learning is to think of the model as the automated version once we decide what features are most important.

Caleb Terrel Orellana says:

thanks broder!

Ma Abrar says:

Is this complete video series?

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