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This meetup focused on everything KubeFlow AI related including Jupyter Notebooks, Kubernetes, TensorFlow, PyTorch, XGBoost, Scikit-Learn, Deep Learning, Machine Learning, and Artificial Intelligence.

Upcoming events (5)

Validating ML Models on Historical Datasets, Luigi Patruno @ MLinProduction.com

Online Workshop - See Details Below

Title Validating Machine Learning Models on Historical Datasets Description Validating machine learning models on historical datasets is the first step in assessing predictive performance. While offline tests demonstrate how a model performs according to evaluation metrics like accuracy or RMSE, these tests can't establish causality between models and user outcomes. When your goal is to use ML to drive specific user behavior, like increasing click-thru rates or engagement, we need online validation. But the ability to validate models in an online manner depends on having the infrastructure to support different model rollout strategies such as blue/green deployments and canary deployments. In this talk we’ll introduce different model rollout strategies and demonstrate how to implement them using Amazon SageMaker. Speaker Bio Luigi Patruno is a Data Scientist and the Founder of MLinProduction.com. He's currently the Director of Data Science at 2U, where he leads a team of data scientists and ML engineers in developing machine learning models and infrastructure to predict student success outcomes. Luigi founded MLinProduction.com to educate data scientists, ML engineers, and ML product managers about best practices for running machine learning systems in production. As a consultant for Fortune 500s and start-ups, Luigi helps companies utilize data science to create competitive advantages. He's taught graduate level courses in Statistics and Big Data Engineering and holds a Masters in Computer Science and a BS in Mathematics. Date/Time 9-10am US Pacific Time (Third Monday of Every Month) RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154 Meetup: https://www.meetup.com/Advanced-KubeFlow/ Zoom: https://zoom.us/j/690414331 Webinar ID:[masked] Phone: [masked] (US Toll) or [masked] (US Toll) Related Links Meetup: https://meetup.com/Advanced-Kubeflow/ GitHub Repo: https://github.com/PipelineAI/ O'Reilly Book: https://datascienceonaws.com YouTube: https://youtube.pipeline.ai Slideshare: https://slideshare.pipeline.ai Support: https://support.pipeline.ai Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227 RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

Kubeflow +Keras/TensorFlow 2.0 +TF Extended (TFX) +Kubernetes +Airflow +PyTorch

Online Workshop - See Details Below

**Title** Hands-on Learning with Kubeflow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + SageMaker + PyTorch + XGBoost + Airflow + MLflow + Apache Spark + Jupyter + TPU + BERT NLP RSVP: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227 **Description** In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), Kubeflow, and Airflow. Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google. Kubeflow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking. Airflow is the most-widely used pipeline orchestration framework in machine learning. **Pre-requisites** Modern browser - and that's it! Every attendee will receive a cloud instance Nothing will be installed on your local laptop Everything can be downloaded at the end of the workshop **Location** Online **Agenda** 1. Create a Kubernetes cluster 2. Install Kubeflow, Airflow, TFX, and Jupyter 3. Setup ML Training Pipelines with Kubeflow and Airflow 4. Transform Data with TFX Transform 5. Validate Training Data with TFX Data Validation 6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and Kubeflow 7. Run a Notebook Directly on Kubernetes Cluster with Kubeflow 8. Analyze Models using TFX Model Analysis and Jupyter 9. Perform Hyper-Parameter Tuning with Kubeflow 10. Select the Best Model using Kubeflow Experiment Tracking 11. Reproduce Model Training with TFX Metadata Store and Pachyderm 12. Deploy the Model to Production with TensorFlow Serving and Istio 13. Save and Download your Workspace **Key Takeaways** Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using modern frameworks and open-source tools. Related Links Meetup: https://meetup.com/Advanced-Kubeflow/ GitHub Repo: https://github.com/PipelineAI/ O'Reilly Book: https://datascienceonaws.com YouTube: https://youtube.pipeline.ai Slideshare: https://slideshare.pipeline.ai Support: https://support.pipeline.ai Monthly Webinar: https://www.eventbrite.com/e/webinar-pipelineai-kubeflow-tensorflow-extended-tfx-airflow-gpu-tpu-spark-ml-tensorflow-ai-tickets-45852865154 RSVP: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227

Kubeflow, GPU, Apache Spark, Kubernetes, Kafka, Scikit, BERT NLP, TensorFlow TFX

Online Workshop - See Details Below

Title Kubeflow, TensorFlow Extended (TFX), Airflow, GPU, TPU, Spark ML, TensorFlow AI, Kubernetes, Kafka, Scikit-Learn, BERT NLP Agenda Hands-on Learning with Kubeflow, TFX, TensorFlow, GPU/TPU, Kafka, Scikit-Learn and JupyterLab running on Kubernetes. Date/Time 9-10am US Pacific Time (Third Monday of Every Month) RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154 Meetup: https://www.meetup.com/Advanced-KubeFlow/ Zoom: https://zoom.us/j/690414331 Webinar ID:[masked] Phone: [masked] (US Toll) or [masked] (US Toll) Related Links Meetup: https://meetup.com/Advanced-Kubeflow/ GitHub Repo: https://github.com/PipelineAI/ O'Reilly Book: https://datascienceonaws.com YouTube: https://youtube.pipeline.ai Slideshare: https://slideshare.pipeline.ai Support: https://support.pipeline.ai Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227 RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

KubeFlow +Keras/TensorFlow 2.0 +TF Extended (TFX) +Kubernetes +Airflow +PyTorch

Online Workshop - See Details Below

**Title** Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + SageMaker + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU RSVP: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227 **Description** In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, and Airflow. Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google. KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking. Airflow is the most-widely used pipeline orchestration framework in machine learning. **Pre-requisites** Modern browser - and that's it! Every attendee will receive a cloud instance Nothing will be installed on your local laptop Everything can be downloaded at the end of the workshop **Location** Online **Agenda** 1. Create a Kubernetes cluster 2. Install KubeFlow, Airflow, TFX, and Jupyter 3. Setup ML Training Pipelines with KubeFlow and Airflow 4. Transform Data with TFX Transform 5. Validate Training Data with TFX Data Validation 6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow 7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow 8. Analyze Models using TFX Model Analysis and Jupyter 9. Perform Hyper-Parameter Tuning with KubeFlow 10. Select the Best Model using KubeFlow Experiment Tracking 11. Reproduce Model Training with TFX Metadata Store and Pachyderm 12. Deploy the Model to Production with TensorFlow Serving and Istio 13. Save and Download your Workspace **Key Takeaways** Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using modern frameworks and open-source tools. Related Links Meetup: https://meetup.com/Advanced-Kubeflow/ GitHub Repo: https://github.com/PipelineAI/ O'Reilly Book: https://datascienceonaws.com YouTube: https://youtube.pipeline.ai Slideshare: https://slideshare.pipeline.ai Support: https://support.pipeline.ai Monthly Webinar: https://www.eventbrite.com/e/webinar-pipelineai-kubeflow-tensorflow-extended-tfx-airflow-gpu-tpu-spark-ml-tensorflow-ai-tickets-45852865154 RSVP: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227

Past events (274)

Kubeflow +Keras/TensorFlow +TFX +Kubernetes +Airflow +PyTorch +SageMaker

Online Workshop - See Details Below

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