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Agile Applied AI Research with Parvez Ahammad - #492

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Manage episode 294962774 series 2355587
Innhold levert av TWIML and Sam Charrington. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av TWIML and Sam Charrington 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.

Today we’re joined by Parvez Ahammad, head of data science applied research at LinkedIn.

In our conversation, Parvez shares his interesting take on organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long-term projects are managed, how to identify products that can help in a cross-cutting way across multiple lines of business, quantitative methodologies to identify unintended consequences in experimentation, and navigating the tension between research and applied ML teams in an organization. Finally, we discuss differential privacy, and their recently released GreyKite library, an open-source Python library developed to support forecasting.

The complete show note for this episode can be found at twimlai.com/go/492.

  continue reading

700 episoder

Artwork
iconDel
 
Manage episode 294962774 series 2355587
Innhold levert av TWIML and Sam Charrington. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av TWIML and Sam Charrington 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.

Today we’re joined by Parvez Ahammad, head of data science applied research at LinkedIn.

In our conversation, Parvez shares his interesting take on organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long-term projects are managed, how to identify products that can help in a cross-cutting way across multiple lines of business, quantitative methodologies to identify unintended consequences in experimentation, and navigating the tension between research and applied ML teams in an organization. Finally, we discuss differential privacy, and their recently released GreyKite library, an open-source Python library developed to support forecasting.

The complete show note for this episode can be found at twimlai.com/go/492.

  continue reading

700 episoder

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