**Open to the Public**
Location: Grand Ballroom H @ Santa Clara Convention Center
Building Scalable ML/AI Pipelines with TFX, KubeFlow, Airflow, and MLflow (Chris Fregly, Founder @ PipelineAI)
In this talk, I build a real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, Airflow, and MLflow.
Described in a 2017 paper from Google, 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
Airflow is the most-widely used pipeline orchestration framework in machine learning and data engineering.
MLflow is a lightweight experiment-tracking system recently open-sourced by Databricks, the creators of Apache Spark. MLflow supports Python, Java/Scala, and R - and offers native support for TensorFlow, Keras, and Scikit-Learn.
Chris Fregly is Founder and Applied AI Engineer at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs." Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
Talk 2: TBD
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