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How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform

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This document summarizes a presentation about utilizing MLFlow and Kubernetes to build an enterprise machine learning platform. It discusses challenges that motivated building such a platform, like lack of model management and difficult deployments. The solution presented abstracts data pipelines into modular components to standardize workflows. It also uses MLFlow to package and track models and experiments, and Kubernetes with Kubeflow to deploy models at scale. A demo shows implementing model serving with these tools.

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Nick Pinckernell, Comcast Applied AI Research Utilizing MLFlow and Kubernetes to build an Enterprise ML Platform #UnifiedAnalytics #SparkAISummit
Topics TOPIC WHY? Example of data pipeline abstraction Modular components and reuse are important for abstracting complex systems Ways to package and track ML project and experiments Consistency and reproducibility is key for scale How Comcast uses Kubeflow to serve and deploy models and pipelines A tangible example to help you brainstorm about your organizations requirements 3#UnifiedAnalytics #SparkAISummit
Challenges and motivations • Before, there was no – model management or tracking – standardization for model packaging or deployments • Cumbersome deployment process – Deployment required code rewrite from research to operations – Days or weeks to deploy • Response and tradeoff: restrict model complexity 4#UnifiedAnalytics #SparkAISummit
Requirements Minimum requirements from our organization • Zero code refactoring or rewriting between research ready models and production • Easier experiment and model tracking • Researchers need to deploy their own models • A/B testing for quick model enhancement testing in production • Ability to modularize and inject custom metrics and workflows at each step 5#UnifiedAnalytics #SparkAISummit
Solution – existing technologies 6#UnifiedAnalytics #SparkAISummit Research Model serving Images, containers
Data pipeline abstraction • Determine use cases • Identify commonalities for modularization • Abstract interfaces • Automate configuration 7#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 8#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 9#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 10#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 11#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 12#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 13#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 14#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 15#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 16#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 17#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 18#UnifiedAnalytics #SparkAISummit
Pipeline abstraction 19#UnifiedAnalytics #SparkAISummit
Seldon inference graphs Allows for complex graphs • A/B testing • Ensembles • Multi-armed bandit • Custom combinations 20#UnifiedAnalytics #SparkAISummit https://github.com/SeldonIO/seldon-core/blob/release-0.2/notebooks/advanced_graphs.ipynb
Packaging and tracking 1. Researchers code and train models with Databricks, Spark 2. Experiments tracked with MLFlow 3. Packaging and model tracking with MLFlow and Kubeflow • MLFLow standard packaging formats • scikit-learn • h2o • TensorFlow • more 21#UnifiedAnalytics #SparkAISummit
An MLFlow experiment 22#UnifiedAnalytics #SparkAISummit
MLFlow – multiple experiments 23#UnifiedAnalytics #SparkAISummit
MLFlow – multiple experiments 24#UnifiedAnalytics #SparkAISummit
MLFlow packaging 25#UnifiedAnalytics #SparkAISummit
Research and model flow 26#UnifiedAnalytics #SparkAISummit
Research and model flow 27#UnifiedAnalytics #SparkAISummit
Research and model flow – at scale 28#UnifiedAnalytics #SparkAISummit
Model serving with Kubeflow Considerations and requirements • Resilient • Highly available • Rate limiting • Shadow deployments • Auto-scaling (WIP) 29#UnifiedAnalytics #SparkAISummit Ambassador http://www.getambassador.io
Throughput Static number of replicas Determined after • Constant and burst load testing with Locust 30#UnifiedAnalytics #SparkAISummit
DEMO A demonstration of • MLFlow experiments – Serving the chosen model • Implementation of components – Consumer pod – Model pod – Producer logic (to simulate real requests) 31#UnifiedAnalytics #SparkAISummit
Choosing the run 32#UnifiedAnalytics #SparkAISummit
Choosing the model 33#UnifiedAnalytics #SparkAISummit
Implementing the model 34#UnifiedAnalytics #SparkAISummit
Implementing the consumer 35#UnifiedAnalytics #SparkAISummit
Implementing the producer 36#UnifiedAnalytics #SparkAISummit
Deploy the model • Define the YAML / JSON Seldon deployment • Build the image – s2i build -E environment_rest . seldonio/seldon-core-s2i-python3:0.6-SNAPSHOT sklearn-iris-mlflow:0.3 • Deploy – kubectl create -f sklearn_iris_deployment.json -n kubeflow 37#UnifiedAnalytics #SparkAISummit
38#UnifiedAnalytics #SparkAISummit Grafana metrics
39#UnifiedAnalytics #SparkAISummit COMCAST IS HIRING PHILADELPHIA WASHINGTON, D.C. SILICON VALLEY DENVER
DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

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