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MLflow: Infrastructure for a Complete Machine Learning Life Cycle

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ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but these platforms are limited to each company’s internal infrastructure. In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

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: Infrastructure for Complete Machine Learning Lifecycle Andrew Chen & Mani Parkhe Sept 6th, 2018
Outline ML development challenges How MLflow tackles these Demo How to get started
Machine Learning Development is Complex
ML Lifecycle 4 Delta Data Prep Training Deploy Raw Data μ λ θ Tuning Scale μ λ θ Tuning Scale Scale Scale Model Exchange Governance
“I build 100s of models/day to lift revenue, using any library: MLlib, PyTorch, R, etc. There’s no easy way to see what data went in a model from a week ago, tune it and rebuild it.” -- Chief scientist at ad tech firm Example
Example “Our company has 100 teams using ML worldwide. We can’t share work across them: when a new team tries to run some code, it often doesn’t even give the same result.” -- Large consumer electronics firm
Custom ML Platforms Facebook FBLearner, Uber Michelangelo, Google TFX +Standardize the data prep / training / deploy loop: if you work with the platform, you get these! – Limited to a few algorithms or frameworks – Tied to one company’s infrastructure Can we provide similar benefits in an open manner?
Introducing Open machine learningplatform • Works with any ML library& language • Runs the same wayanywhere (e.g. any cloud) • Designed to be useful for 1 or 100,000 person orgs
MLflow Components 9 Tracking Record and query experiments: code, configs, results,…etc Projects Packaging format for reproducible runs on any platform Models General model format that supports diverse deployment tools
Demo
bathrooms: 1 bedrooms: 2 accommodates: 4 total_reviews: 45 cleanliness_rating:9 location_rating:10 checkin_rating: 10 zip_code: 94105 price: 150 f (x) Model listing attributes based on data from insideairbnb.com Goal: Predict Price of Airbnb Listings
Notebooks LocalApps CloudJobs Tracking Server UI API MLflow Tracking Python or REST API
Project Spec Code DataConfig LocalExecution Remote Execution MLflow Projects
Model Format Flavor 2Flavor 1 Run Sources InferenceCode Batch & Stream Scoring Cloud ServingTools MLflow Models Simple model flavors usableby many tools
Getting Started with MLflow Install with pip install mlflow Find detailed tutorials at mlflow.org Sign up at databricks.com/mlflow for futureupdates
Ongoing MLflow Roadmap • TensorFlow, Keras, PyTorch, H2O, MLlib integrations ✔ • Java and R language APIs (both in review!) • Multi-step workflows • Hyperparameter tuning • Data source API based on Spark data sources • Model metadata & management
Conclusion Workflow tools can greatly simplifythe ML lifecycle • Improve usability for both data scientists and engineers • Same way software dev lifecycle tools simplify development Learn more about MLflow at mlflow.org
Thank you!
https://databricks.com/sparkaisummit/europe 30% Discount Code: Mani30
MLflow Design Philosophy 1. “API-first”, open platform • Allow submittingruns,models,etc from anylibrary & language • Example: a “model” can justbe a lambdafunction thatMLflow can thendeploy in many places (Docker, AzureML, Spark UDF, …) Key enabler: built aroundREST APIs and CLI
MLflow Design Philosophy 2. Modular design • Let people use different components individually(e.g.,use MLflow’s project format but not its deployment tools) • Easy to integrateinto existing ML platforms & workflows Key enabler: distinct components (Tracking/Projects/Models)

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