Accelerating Production Machine Learning with MLflow with Matei Zaharia
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The document discusses MLflow, an open-source machine learning platform that supports any ML library and is designed to manage the machine learning lifecycle from data preparation to deployment. It highlights MLflow's features such as tracking experiments, model packaging, and integration with various cloud services and environments. The latest version, 0.7.0, introduces new functionalities and partnerships, aiming to simplify ML development for both data scientists and engineers.
Accelerating Production Machine Learning with MLflow with Matei Zaharia
- 1. : Accelerating Production Machine Learning Matei Zaharia @matei_zaharia
- 2. Machine Learning Development is Complex
- 3. ML Lifecycle 3 Delta Data Prep Training Deploy Raw Data μ λ θ Tuning Scale μ λ θ Tuning Scale Scale Scale Model Exchange Governance
- 4. Custom ML Platforms Facebook FBLearner, Uber Michelangelo, Google TFX Standardize the data prep / training / deploy cycle: if you work within 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?
- 5. www.mlflow.org Introducing Open source machine learning platform • Works with any ML library & language • Runs the same way everywhere & cross-cloud • Scales to big data with Apache Spark Launched this June • Already 48 contributors and many new features! This Talk: MLflow Overview + New Announcements
- 6. How Does MLflow Work? 8 Tracking Record and query experiments: code, data, config, results Projects Package workflows into reproducible and reusable steps Models Model packaging format for diverse deployment tools
- 7. Model Development without MLflow data = load_text(file) ngrams = extract_ngrams(data, N=n) model = train_model(ngrams, learning_rate=lr) score = compute_accuracy(model) print(“For n=%d, lr=%f: accuracy=%f” % (n, lr, score)) pickle.dump(model, open(“model.pkl”)) For n=2, lr=0.1: accuracy=0.71 For n=2, lr=0.2: accuracy=0.79 For n=2, lr=0.5: accuracy=0.83 For n=2, lr=0.9: accuracy=0.79 For n=3, lr=0.1: accuracy=0.83 For n=3, lr=0.2: accuracy=0.82 For n=4, lr=0.5: accuracy=0.75 ... What if I expand the input data? What if I tune this other parameter? What if I upgrade my ML library? What version of my code was this result from?
- 8. Model Deployment without MLflow Code & Models DATA SCIENTIST PRODUCTION ENGINEER Please deploy this SciKit model! Please deploy this Spark model! Please deploy this R model! Please deploy this TensorFlow model! Please deploy this ArXiv paper! …
- 9. Model Development with MLflow data = load_text(file) ngrams = extract_ngrams(data, N=n) model = train_model(ngrams, learning_rate=lr) score = compute_accuracy(model) print(“For n=%d, lr=%f: accuracy=%f” % (n, lr, score)) pickle.dump(model, open(“model.pkl”))
- 10. $ mlflow ui Model Development with MLflow data = load_text(file) ngrams = extract_ngrams(data, N=n) model = train_model(ngrams, learning_rate=lr) score = compute_accuracy(model) mlflow.log_param(“data_file”, file) mlflow.log_param(“n”, n) mlflow.log_param(“learning_rate”, lr) mlflow.log_metric(“score”, score) mlflow.sklearn.log_model(model) Track parameters, metrics, output files & code version Search using UI or API
- 11. MLflow UI: Inspecting Runs
- 12. MLflow UI: Comparing Runs
- 13. Project Spec Code ConfigDeps Local Execution Remote Cluster Packaging Code: MLflow Projects $ mlflow run git://...
- 14. Model Format ONNX Flavor Python Flavor Training Apps Batch & Stream Scoring REST Serving Tools Packaging Models: MLflow Models Packaging Format . . . Inference Code MLlib
- 15. Model Deployment with MLflow DATA SCIENTIST PRODUCTION ENGINEER Please deploy this MLflow Model! OK, it’s up in our REST server & Spark! Please run this MLflow Project nightly for updates! Don’t even tell me what ArXiv paper that’s from...
- 16. MLflow Development Status Many new features since our release in June • Model packaging for MLlib, H2O, TensorFlow, PyTorch, Keras • Storage on Azure, AWS, Google, SFTP • Java and Scala API • New examples and UI features Just released MLflow 0.7.0 today!
- 17. Major Announcement in MLflow 0.7.0 + RStudio partnered with Databricks to add an MLflow R API See Kevin Kuo’s talk on this at 14:40 today!
- 18. Conclusion Workflow platforms can greatly simplify ML development • Improve usability for both data scientists and engineers Get started at mlflow.org, and come see our other talks: • MLflow R API: 14:40 today • ML Factories: 11:40 Thursday • 1h Deep Dive: 14:00 Thursday