AI democratization is a well understood term that is often associated with enabling learning and adoption of AI. In the industrial world, BHGE has been at the forefront of democratizing AI. In contrast to commercial solutions that focus on traditional machine learning and even deep learning techniques, BHGE has focused on techniques for missing data estimation, modeling, tag mapping, classification etc. that are particularly needed in the industrial world. This has led to democratization of a wide variety of techniques from Gaussian Process models to topological clustering and probabilistic analysis. These have been made available through an AI Workbench where citizen data scientists can bring in data, build and customize models, setup custom visualizations to interact with results and share models. Underlying the AI Workbench is a Kubernetes-powered implementation on GCP, managing 1000s of containers. In this talk, we will highlight the power of enabling non-data scientist engineers with advanced analytics capabilities to build and maintain sophisticated models at scale. We will also highlight the impressive scaling capabilities of GCP to enable modeling at a massive scale – 100,000 models built live under 5 minutes on stage!
Democratizing AI for Industrial Applications → http://bit.ly/2KFTbnZ
Watch more:
Next ’19 ML & AI Sessions here → https://bit.ly/Next19MLandAI
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions
Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform
Speaker(s): Arun Subramaniyan
Session ID: MLAI227
product:TensorFlow;
Duration: 42:36
Publisher: Google Cloud
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