Artwork

Innhold levert av The Oakmont Group and John Gilroy. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av The Oakmont Group and John Gilroy eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
Player FM - Podcast-app
Gå frakoblet med Player FM -appen!

Ep 198 Creating a Solid Foundation for AI

22:23
 
Del
 

Manage episode 452002984 series 3610832
Innhold levert av The Oakmont Group and John Gilroy. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av The Oakmont Group and John Gilroy eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Connect to John Gilroy on LinkedIn https://www.linkedin.com/in/john-gilroy/

Want to listen to other episodes? www.Federaltechpodcast.com

When Qlik was founded in 1993, hard drives were measured in megabytes, and the Internet was primarily text-based. If lucky, you could get information in structured columns and formats.

Fast forward thirty years, and some estimate YouTube alone has 4.3 petabytes of data loaded every day.

The federal government certainly has its share of formatted data. A recent survey showed that 80% of data collected by the federal government is unstructured. This is information like text files, videos, or emails that are stored in many formats. As a result, it isn't easy to store and manage.

This has a real impact when an organization tries to take advantage of Artificial Intelligence.

Today, we sit down with Andrew Churchill to discuss creating a solid data foundation for AI. We detail topics like data movement, data streaming, and data quality during the discussion.

He differentiates between data lakes and data warehouses as strategies for handling all the unstructured data used for training AI models.

  continue reading

202 episoder

Artwork
iconDel
 
Manage episode 452002984 series 3610832
Innhold levert av The Oakmont Group and John Gilroy. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av The Oakmont Group and John Gilroy eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Connect to John Gilroy on LinkedIn https://www.linkedin.com/in/john-gilroy/

Want to listen to other episodes? www.Federaltechpodcast.com

When Qlik was founded in 1993, hard drives were measured in megabytes, and the Internet was primarily text-based. If lucky, you could get information in structured columns and formats.

Fast forward thirty years, and some estimate YouTube alone has 4.3 petabytes of data loaded every day.

The federal government certainly has its share of formatted data. A recent survey showed that 80% of data collected by the federal government is unstructured. This is information like text files, videos, or emails that are stored in many formats. As a result, it isn't easy to store and manage.

This has a real impact when an organization tries to take advantage of Artificial Intelligence.

Today, we sit down with Andrew Churchill to discuss creating a solid data foundation for AI. We detail topics like data movement, data streaming, and data quality during the discussion.

He differentiates between data lakes and data warehouses as strategies for handling all the unstructured data used for training AI models.

  continue reading

202 episoder

Alle episoder

×
 
Loading …

Velkommen til Player FM!

Player FM scanner netter for høykvalitets podcaster som du kan nyte nå. Det er den beste podcastappen og fungerer på Android, iPhone og internett. Registrer deg for å synkronisere abonnement på flere enheter.

 

Hurtigreferanseguide

Copyright 2024 | Sitemap | Personvern | Vilkår for bruk | | opphavsrett