January 14, 2020

99 words 1 min read

Customized ML for the enterprise (sponsored by Google Cloud)

Customized ML for the enterprise (sponsored by Google Cloud)

Levent Besik explains how can enterprises stay ahead of the game with customized ML in our ever-changing world of AI capabilities and limited data science resources.
Talk Title Customized ML for the enterprise (sponsored by Google Cloud)
Speakers Levent Besik (Google)
Conference Artificial Intelligence Conference
Conf Tag Put AI to Work
Location San Francisco, California
Date September 5-7, 2018
URL Talk Page
Slides
Video Talk Video

Levent Besik explains how can enterprises stay ahead of the game with customized ML in our ever-changing world of AI capabilities and limited data science resources. This keynote is sponsored by Google Cloud.

google ml data science ai cloud
comments powered by Disqus
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data

Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data

January 13, 2020

High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsofts Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNNboth on single VMs and at scale on GPU clusters.
Hit a home run making baseball decisions using artificial intelligence and machine learning

Hit a home run making baseball decisions using artificial intelligence and machine learning

January 12, 2020

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and moreusing real-life baseball data to illustrate the concepts.
Kubeflow: Portable machine learning on Kubernetes (sponsored by Google)

Kubeflow: Portable machine learning on Kubernetes (sponsored by Google)

January 11, 2020

Michelle Casbon offers an overview of Kubeflow. By providing a platform that reduces variability between services and environments, Kubeflow enables applications that are more robust and resilient, resulting in less downtime, quality issues, and customer impact. It also supports the use of specialized hardware such as GPUs, which can reduce operational costs and improve model performance.
Open source decentralized data markets for training AI in areas of large shared risk

Open source decentralized data markets for training AI in areas of large shared risk

January 11, 2020

Paco Nathan examines decentralized data markets. With components based on blockchain technologiessmart contracts, token-curated registries, DApps, voting mechanisms, etc.decentralized data markets allow multiple parties to curate ML training datasets in ways that are transparent, auditable, and secure and allow equitable payouts that take social values into account.
Pangeo: Big data climate science in the cloud

Pangeo: Big data climate science in the cloud

January 6, 2020

Climate science is being flooded with petabytes of data, overwhelming traditional modes of data analysis. The Pangeo project is building a platform to take big data climate science into the cloud using SciPy and large-scale interactive computing tools. Join Ryan Abernathey and Yuvi Panda to find out what the Pangeo team is building and why and learn how to use it.
Tutorial: Kubeflow End-to-End: GitHub Issue Summarization

Tutorial: Kubeflow End-to-End: GitHub Issue Summarization

January 5, 2020

IMPORTANT NOTE:Due to the nature of tutorials, this session has been placed in a smaller capacity room to help facilitate a conducive learning environment. Space is very limited and seating will be g …