Christine Görner Video
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Robert Crowe Crowe Goto Tierney Munn Görner 2019
This presentation was recorded at GOTO Copenhagen 2019. #GOTOcon #GOTOcph (http•••) Robert Crowe - TensorFlow Developer Advocate ORIGINAL TALK TITLE Taking Machine Learning Research to Production: Solving Real Problems ABSTRACT Most of the focus in the ML community is on research, which is exciting and important. Equally important however is bringing that research to production applications to solve real-world problems, but the issues and approaches for doing that are often poorly understood. An ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed and trained using tools like notebooks and suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components. In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments. The user experience of any ML application is unique to the model’s performance on that user’s input data, so if the model doesn’t perform well on that particular data segment then the user has a poor experience. We discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX). Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology including testability, hot versioning, and deep performance analysis. Robert Crowe is a data scientist and TFX Developer Advocate at Google and will discuss how developers can move their ML [...] Download slides and read the full abstract here: (http•••) RECOMMENDED BOOKS Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • (http•••) Phil Winder • Reinforcement Learning • (http•••) Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • (http•••) Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • (http•••) Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • (http•••) Aurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow • (http•••) (http•••) (http•••) (http•••) #ML #TensorFlow #TFX #TensorFlowExtended #MachineLearning Looking for a unique learning experience? Attend the next GOTO Conference near you! Get your ticket at (http•••) SUBSCRIBE TO OUR CHANNEL - new videos posted almost daily. (http•••)
Robert Crowe Crowe Goto Tales Tierney Munn Görner 1643 1689 1820 1916 2021
This presentation was recorded at GOTOpia February 2021. #GOTOcon #GOTOpia (http•••) Robert Crowe - TensorFlow Developer Advocate at Google ABSTRACT A machine learning (ML) journey typically starts with trying to understand the world, and looking for data that describes it. This leads to an experimentation phase, where we try to use that data to model the parts of the world that we’re interested in, often because they directly affect our users or our business. Once we have one or more models that deliver good results, it’s time to move those models into production. Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. This is especially true for maintaining and improving model performance over the lifetime of a production application. Unfortunately, the issues involved and approaches available are often poorly understood. A ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed using tools and systems which suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components. In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments. In this talk, Robert will discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow. Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology [...] TIMECODES 00:00 Intro 02:15 Production ML 05:41 We need MLOps 06:21 Continuous integration, deployment and testing 07:29 MLOps level 0: Manual Process 09:02 Experiment 12:11 Tales from the trenches 13:02 TensorFlow Extended (TFX) 14:28 TFX production components 16:43 What is a TFX component? 18:20 TFX orchestration 19:16 Difference between TFX & Kubeflow pipelines 23:00 Distributed pipeline processing: Apache Beam 25:28 TFX standard components 25:53 Components: ExampleGen, StatisticsGen & SchemaGen 28:17 Components: ExampleValidator, Transform & Trainer 31:45 Components: Tuner, Evaluator & InfraValidator 32:51 Components: Pusher & BulkInferrer 33:37 TFX pipeline nodes 34:43 TRFX custom components 36:09 Very high level architecture 37:03 Outro Download slides and read the full abstract here: (http•••) RECOMMENDED BOOKS Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • (http•••) Phil Winder • Reinforcement Learning • (http•••) Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • (http•••) Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • (http•••) Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • (http•••) Aurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow • (http•••) (http•••) (http•••) (http•••) #MachineLearning #ML #TensorFlow #TF #TFX #TensorFlowExtended #Kubeflow #AI #ArtificialIntelligence #DataScience #MLOps #CI #ContinuousIntegration #Testing #Orchestration #ApacheBeam #ExampleGen #StatisticsGen #SchemaGen Looking for a unique learning experience? Attend the next GOTO conference near you! Get your ticket at (http•••)h SUBSCRIBE TO OUR CHANNEL - new videos posted almost daily. (http•••)
Rudolf Schock Christine Görner Glück Blatt Lauf Heisser Wilder 1958
Rudolf Schock - Mir geht's gut & Christine Görner - Höre ich Zigeunergeigen 1958 (Filmausschnitt aus der Operette 'Gräfin Mariza') 1. Rudolf Schock - Mir geht's gut Mir geht's gut, sag ich mir täglich wenn's auch nicht .. Denn das schwerste ist erträglich wenn man's leicht nimmt Hab und Gut geht wie der Wind weht auf der welt ist alles möglich weil sie sich dreht Glück ist kein Blatt im Wind flattert vorbei geschwind aber danach zu jagen das hat keinen Sinn Ja ist der Teufel los lach' ich ja zweifellos - hahaha denn bei mir ist alles möglich weil ich halt so bin 2. Christine Görner - Höre ich Zigeunergeigen Höre ich Zigeunergeigen, bei des Cymbals wildem Lauf, wird es mir ums Herz so eigen, wachen alle Wünsche auf. Klingt ein heisser Csardastraum sinnbetörеnd durch den Raum, klingt ein toller, sehnsuchtsvoller, heisser, wilder Csardastraum. Wo wohnt die Liebe, wer kann' s mir sagen, wo wohnt die Liebe, wen soll ich fragen? Einmal das Herz in tollеr Lust verschenken, küssen, küssen und nicht denken! Einmal nur glücklich sein! Wo wohnt die Liebe, wer kann's ergründen, wo wohnt die Liebe, wer kann sie finden? Nur einmal küssen, bis der Liebe Flammen schlagen über mir zusammen! Einmal nur glücklich sein! Nur einmal küssen, bis der Liebe Flammen schlagen über mir zusammen! Einmal, einmal nur glücklich sein!
Christine Görner Frühling Kollo Walter Kollo 1944 1959 1996
Film Was eine Frau im Frühling träumt 1959 'Was eine Frau im Frühling träumt' ist ein deutscher Liebesfilm unter der Regie von Erik Ode. Schauspieler: Winnie . Christine Görner - Was. Christine Görner - Was eine Frau im Frühling träumt 1959 Finale aus dem gleichnamigen Film, mit Winnie Markus, Rudolf Prack, Claus Biederstaedt und Chariklia Baxevanos Vorlage aus der. Beschrijving. Spielfilm von 1944. Rene Kollo - Was eine Frau im Frühling träumt 1996 (aus der Operette 'Marietta' von Walter Kollo) Was eine Frau im Frühling träumt, ist ach so dumm und ungereimt. Doch kommen erst.
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- cronologia: Cantanti lirici (Europa).
- Indici (per ordine alfabetico): G...