Massive Parallel and Elastic AI Pipelines with TensorFlow and Kubernetes

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146 Personen haben teilgenommen


17:30 Welcome and Pizza
18:00 Fast Track Intro to DeepLearning
18:30 What's new in TensorFlow[masked]:15 Build Enterprise AI Pipelines using KubeFlow

Sponsored by:
1) The IBM Data Science Community, where data scientists and developers to learn, share, and engage with their peers and industry renowned data scientists => Pizza

2) IBM Codait (Center for Open Source Data and AI Technologies) => Speaker Travel

3) TNG Technology Consulting => Drinks, Location

Talk Summary:
Toward the end of 2015, Google released TensorFlow, which started out as just another numerical library, but has grown to become a de facto standard in AI technologies. TensorFlow received a lot of hype as part of its initial release, in no small part because it was released by Google. Despite the hype, there have been complaints on usability. Especially, for example, the fact that debugging was only possible after construction of the static execution graph. In addition to that, neural networks needed to be expressed as a set of linear algebra operations which was considered too low level by many practitioners. PyTorch and Keras addressed many of the flaws in TensorFlow and gained a lot of ground. TensorFlow 2.0 successfully addressed those complaints and promises to become the go-to framework for many AI problems.

Romeo Kienzler introduces you to the most prominent changes in TensorFlow 2.0 and how you can use these new features successfully in your projects. He explores eager execution, parallelization strategies, the advantages of the tight high-level Keras integration, live neural network training monitoring using TensorBoard, automated hyper parameter optimization, model serving with TensorFlow service, TensorFlow.js, and TensorFlow Lite. He shares an outlook on TFX—where Google is planning to open source its complete AI pipeline—and contrasts it with existing de facto standard frameworks like Apache Spark.

Speaker Bio:
Romeo Kienzler is Chief Data Scientist at the IBM Center for Open Source Data and AI Technologies (CODAIT) in San Fransisco, owning the strategy lead for AI Model Training. He works as Associate Professor for artificial intelligence at the Swiss University of Applied Sciences Berne and his current research focus is on cloud-scale machine learning and deep learning using open source technologies including TensorFlow, Keras, DeepLearning4J, Apache SystemML and the Apache Spark stack. He also contributes to various open source projects. He regularly speaks at international conferences including significant publications in the area of data mining, machine learning and Blockchain technologies.


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17:30 Welcome and Pizza
18:00 What's new in TensorFlow[masked]:30 Build Enterprise AI Pipelines using KubeFlow