Deploying Deep Learning Pipelines on KubeFlow @ Comcast AI

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Talk 1: Deploying Deep Learning Pipelines on KubeFlow @ Comcast AI

In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment.

This is accomplished using a range of tools and frameworks such as Kubeflow, MLflow, Apache Spark and others. With a machine learning environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority.

The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes.

The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.

Talk 2: Building Scalable ML/AI Pipelines with TFX, KubeFlow, Airflow, and MLflow

In this talk, I build a real-world machine learning pipeline 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
model tracking.

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.