Language, Graphs, and AI in Industry

Name: Language, Graphs, and AI in Industry
Uploaded: 2024-01-05
Description: Let's talk about key findings from these conferences, specifically summarizing teams that have ROI on machine learning in production: what are the things in common they're doing, and what are the most important caveats they urge other teams to consider when getting started? Because these key takeaways aren't found in the current AI news cycle.

Posted Jan 05, 2024 | Views 861

# ROI on ML

# AI in Industry

# Derwen, Inc

SPEAKERS

Paco Nathan

Managing Partner @ Derwen, Inc.

Expertise in machine learning, natural language, graph technologies, distributed systems, and related topics. Known as a "player/coach", with +40 years tech industry experience, ranging from Bell Labs to early-stage start-ups. Started coding in 1972; used email for work since 1981; software lead for neural network hardware accelerators 1989-96; guinea pig for AWS 2005-ff; led early teams identified as "Data Science" in 2009-ff.

Board member for Argilla.io; Advisor for KUNGFU.AI, DataSpartan.co.uk; Lead committer on pytextrank and kglab. Formerly: Director, Community Evangelism for Apache Spark at Databricks.

Werner Herzog is his spirit animal.

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Board member for Argilla.io; Advisor for KUNGFU.AI, DataSpartan.co.uk; Lead committer on pytextrank and kglab. Formerly: Director, Community Evangelism for Apache Spark at Databricks.

Werner Herzog is his spirit animal.

+ Read More

Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

Let's talk about key findings from these conferences, specifically summarizing teams that have ROI on machine learning in production: what are the things in common they're doing, and what are the most important caveats they urge other teams to consider when getting started? Because these key takeaways aren't found in the current AI news cycle.

+ Read More