Download as PDF, PPTX
Uploaded byDatabricks
Managing the Complete Machine Learning Lifecycle with MLflow
The document discusses standardizing the machine learning lifecycle using MLflow, an open-source framework introduced in June 2018 for managing experiments, projects, and model deployment. MLflow aims to address the complexities and silos in machine learning by providing solutions for tracking experiments, reproducible projects, and model deployment across various platforms. The framework has gained rapid community adoption and offers features like managed tracking servers, integration with cloud storage, and support for diverse deployment tools.
More Related Content
Similar to Managing the Complete Machine Learning Lifecycle with MLflow
![[DSC DACH 23] Go with the flow – Track your machine learning lifecycle using ...](https://cdn.slidesharecdn.com/ss_thumbnails/pspddscdachmlflow-230424084504-4f833f21-thumbnail.jpg?width=640&height=640&fit=bounds)
Managing the Complete Machine Learning Lifecycle with MLflow
- 1. Standardising machine learning lifecycleon Mlflow Thunder Shiviah & Michael Shtelma SAIS EUROPE - 2019 1
- 2. ML Code Configuration Data Collection Data Verification Feature Extraction Machine Resource Management Analysis Tools Process ManagementTools Serving Infrastructure Monitoring “Hidden Technical Debt in Machine Learning Systems,” Google NeurIPS 2015 Figure 1: Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small green box in the middle. The required surrounding infrastructure is vast and complex. Hardest Part of ML isn’t ML, it’s Data
- 3. DATA ENGINEERS x Data & MLTech and People are in Silos DATA SCIENTISTS
- 4. ML Lifecycle isManual, Inconsistent and Disconnected ● Ad hoc approach to track experiments ● Very hard to reproduce experiments Prep Data ● Multiple tightly coupled deployment options ● Different monitoring approach for each framework Build Model Deploy Model ● Lowlevel integrations for Data and ML ● Difficult to track data used for a model
- 5.
- 6. What is MLflow? Unveiled in June 2018, MLflow is an open source framework to manage the complete Machine Learning Lifecycle. Tracking Record and query experiments: code, data, config, results Projects Packaging format for reproducible runs on any platform Models General model format that supports diverse deployment tools
- 7. MLflow Momentum ata glance
- 8. Rapid Community Adoption ●Time till 74 contributors: Spark = 3 years; MLflow = 8 months
- 9.
- 10. Notebooks Local Apps Cloud Jobs TrackingServer UI API MLflow Tracking Python or REST API
- 11. Key Concepts inTracking • Parameters: key-value inputs to your code • Metrics: numeric values (can update over time) • Artifacts: arbitrary files, including models • Source: what code ran?
- 12. Experiment Tracking withManaged MLflow Record runs, and keep track of models parameters, results, code, and data from each experiment in one place. Provides: ● Pre-configured MLflow tracking server ● Databricks Workspace & Notebooks UI integration ● S3, Azure Blob Storage, Google Cloud for artifacts storage ● Experiments management via role based Access Control Lists (ACLs)
- 13.
- 14. Project Spec Code DataConfig LocalExecution Remote Execution MLflow Projects
- 15. Example MLflow Project my_project/ ├──MLproject │ │ │ │ │ ├── conda.yaml ├── main.py └── model.py ... conda_env: conda.yaml entry_points: main: parameters: training_data: path lambda: {type: float, default: 0.1} command: python main.py {training_data} {lambda} $ mlflow run git://<my_project> mlflow.run(“git://<my_project>”, ...)
- 16. Reproducible Projects withManaged MLflow Build composable projects, capture dependencies and code history for reproducible results, and share projects with peers. Provides: ● Support for Git, Conda, and other file storage systems ● Remote execution via command line as a Databricks Job
- 17.
- 18. Model Format Flavor 2Flavor1 Run Sources Inference Code Batch & Stream Scoring Cloud Serving Tools MLflow Models Simple model flavors usable by many tools
- 19. Example MLflow Model my_model/ ├──MLmodel │ │ │ │ │ └── estimator/ ├── saved_model.pb └── variables/ ... Usable by tools that understand TensorFlow model format Usable by any tool that can run Python (Docker, Spark, etc!) run_id: 769915006efd4c4bbd662461 time_created: 2018-06-28T12:34 flavors: tensorflow: saved_model_dir: estimator signature_def_key: predict python_function: loader_module: mlflow.tensorflow
- 20. Model Deployment withManaged MLflow Quickly deploy models to any platform based on your needs, locally or in the cloud, from experimentation to production. Supports: ● Databricks Jobs and Clusters for Production Model Operations ● Batch inference on Databricks (Apache Spark) ● REST endpoints via Docker containers, Azure ML, or SageMaker
- 21.
- 22.
- 23. Model registry &deployment tracking https://databricks.com/sparkaisummit/north-america/2019-spark-summit-ai-keynotes-2#keynote-e
- 24.
- 25.
- 26.