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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MLFlow: Platform for Complete Machine Learning Lifecycle Databricks Description
Data Science and ML development bring 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.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
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 even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
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With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
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MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks 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 even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce 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. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
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Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
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Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
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As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
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The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
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Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
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· Precautions for retries and speculative execution
· Pipelining to improve performance
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(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
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This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
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What are we storing?
Multi Source – Multi Channel Problem
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Performance Trade Offs with Various formats
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(String FTW)
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Writer Worries and How to Wipe them Away
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Performance Time!
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Professional Photographers:
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MLflow: Infrastructure for a Complete Machine Learning Life Cycle
- 1. : Infrastructure for Complete Machine Learning Lifecycle Andrew Chen & Mani Parkhe Sept 6th, 2018
- 2. Outline ML development challenges How MLflow tackles these Demo How to get started
- 3. Machine Learning Development is Complex
- 4. 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
- 5. “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
- 6. 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
- 7. 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?
- 8. 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
- 9. 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
- 10. Demo
- 11. 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
- 13. Project Spec Code DataConfig LocalExecution Remote Execution MLflow Projects
- 14. Model Format Flavor 2Flavor 1 Run Sources InferenceCode Batch & Stream Scoring Cloud ServingTools MLflow Models Simple model flavors usableby many tools
- 15. Getting Started with MLflow Install with pip install mlflow Find detailed tutorials at mlflow.org Sign up at databricks.com/mlflow for futureupdates
- 16. 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
- 17. 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
- 18. Thank you!
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- 20. 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
- 21. 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)