尊敬的 微信汇率：1円 ≈ 0.046166 元 支付宝汇率：1円 ≈ 0.046257元 [退出登录]
SlideShare a Scribd company logo

MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani Parkhe and Tomas Nykodym

•
•
Databricks
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: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Understand aspects of MLflow APIs Using tracking APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics Package, save, and deploy an MLflow model Serve it using MLflow REST API What’s next and how to contribute

Data & Analytics
1 of 41
Download to read offline
: Platform for Complete Machine Learning Lifecycle Mani Parkhe & Tomas Nykodym Oct 4, 2018
Outline Overview of ML development challenges How MLflow tackles these MLflow components How to get started
Machine Learning Development is Complex
ML Lifecycle 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 learning platform • Works with any ML library & language • Runs the same way anywhere (e.g. any cloud) • Designed to be useful for 1 or 1000+ person orgs
MLflow Components 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
MLflow Tracking Tracking Record and query experiments: code, configs, results, …etc import mlflow with mlflow.start_run(): mlflow.log_param("layers", layers) mlflow.log_param("alpha", alpha) # train model mlflow.log_metric("mse", model.mse()) mlflow.log_artifact("plot", model.plot(test_df)) mlflow.tensorflow.log_model(model)
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 Local Apps Cloud Jobs Tracking Server UI API Python or REST API
MLflow backend stores 1. Entity Store • FileStore (local and REST) • Database backed (coming soon) 2. Artifact Repository • S3 backed store • Azure Blob storage • Google Cloud storage • DBFS artifact repo
Learning More About MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 16
Break
MLflow Deep Dive Part #2 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 Closer look at MLflow Projects and Models
MLflow Deep Dive Part #2 2.1 MLflow Projects • Motivation • Quick Overview • Examples • Demo 2.2 MLflow Models • Motivation • Quick Overview • About Flavors • Examples • Demo Outline
2.1 MLflow Projects • Motivation • Quick Overview • Examples • Demo
MLflow Projects Motivation Diverse set of tools Diverse set of environments Result: It is difficult to productionalize and share.
Project Spec Code DataConfig Local Execution Remote Execution
MLflow Projects Packaging format for reproducible ML runs - Any code folder or Github repository - Optional MLproject file with project configuration Defines dependencies for reproducibility - Conda (+ R, Docker, …) dependencies can be specified in MLproject - Reproducible in (almost) any environment Execution API for running projects locally or remote - CLI / Python / R / Java - mlflow run . -e train … - mlflow.projects.run(., “train”, ...)
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>”, ...)
Airbnb Example Revisited We have 2 other data scientists working on the project • Aaron used XGBoost in python • Amy used Keras in R • They provided their code as MLflow project on github Let’s test their code!
2.2 MLflow Models • Motivation • Quick Overview • About Flavors • Examples • Demo
Inference Code Batch & Stream Scoring Serving ToolsML Frameworks
Model Format Flavor 2Flavor 1 ML Frameworks Inference Code Batch & Stream Scoring Serving Tools Standard for ML models
MLflow Models Packaging format for ML Models - Any directory with MLmodel file - Different flavors for different models Defines dependencies for reproducibility - Conda (+ R, Docker, … ?) dependencies can be specified in MLproject - Deployable (almost) anywhere Deployment APIs - CLI / Python / R / Java - mlflow pyfunc serve … - mlflow.pyfunc.load(...).predict()
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 >>> mlflow.tensorflow.log_model(...)
About Those Flavors Models can generate several flavors • Spark ML model generates spark, mleap and pyfunc flavors Generic flavors provide abstraction layer • pyfunc can be served locally, as spark_udf, on Azure ML, … • By generating pyfunc flavor we get all above Different Flavors Can be Loaded By Different Languages • pyfunc in python, mleap in Java, crate in R, Keras in python and R
PyFunc - Generic Python Model PyFunc is saved as “just a directory”: ./model_dir/ ./MLmodel: configuration <code>: code packaged with the model (specified in the MLmodel file) <data>: data packaged with the model (specified in the MLmodel file) <env>: Conda environment definition (specified in the MLmodel file) Model loader specified in MLmodel file: Arbitrary python code loaded dynamically at runtime Loaded Pyfunc is “just an object” with a predict method: predict(pandas.DataFrame) -> pandas.DataFrame | numpy.array
Airbnb Example Revisited (Again) Let’s put our models to production! • Predict from cli • Set up a real time REST API prediction server • Score big data in Spark
Advanced MLFlow - HyperParameters 34 HyperParam Search Run Train Model Run mlflow.log_metric()mlflow.get_metric() Projects Tracking Models mlflow run ... Logged Model mlflow.log_artifact
Advanced MLFlow - Multistep Workflow
Get started with MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 36
Ongoing MLflow Roadmap •TensorFlow, Keras, PyTorch, H2O, MLlib integrations ✔ •Java and R language APIs ✔ •Multi-step workflows •Hyperparameter tuning •Data source API based on Spark data sources •Model metadata & management
Conclusion Workflow tools can greatly simplify the 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!
MLflow Design Philosophy 1. “API-first”, open platform • Allow submitting runs, models, etc from any library & language • Example: a “model” can just be a lambda function that MLflow can then deploy in many places (Docker, Azure ML, Spark UDF, …) Key enabler: built around REST 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 integrate into existing ML platforms & workflows Key enabler: distinct components (Tracking/Projects/Models)

Recommended

Introduction to MLflow
Introduction to MLflowIntroduction to MLflow
Introduction to MLflow
Databricks
 
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleMLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Databricks
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflow
Databricks
 
Simplifying Model Management with MLflow
Simplifying Model Management with MLflowSimplifying Model Management with MLflow
Simplifying Model Management with MLflow
Databricks
 
Managing the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflowManaging the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflow
Databricks
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
DataWorks Summit
 
Pythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowPythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlow
Fernando Ortega Gallego
 
MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle
Databricks
 

Recommended

Introduction to MLflow
Introduction to MLflowIntroduction to MLflow
Introduction to MLflow
Databricks
 
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleMLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Databricks
 
MLOps Using MLflow
MLOps Using MLflowMLOps Using MLflow
MLOps Using MLflow
Databricks
 
Simplifying Model Management with MLflow
Simplifying Model Management with MLflowSimplifying Model Management with MLflow
Simplifying Model Management with MLflow
Databricks
 
Managing the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflowManaging the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflow
Databricks
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
DataWorks Summit
 
Pythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlowPythonsevilla2019 - Introduction to MLFlow
Pythonsevilla2019 - Introduction to MLFlow
Fernando Ortega Gallego
 
MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle
Databricks
 
MLflow with Databricks
MLflow with DatabricksMLflow with Databricks
MLflow with Databricks
Liangjun Jiang
 
mlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecyclemlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecycle
Databricks
 
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Databricks
 
"Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow""Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow"
Databricks
 
MLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine LearningMLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine Learning
Matei Zaharia
 
MLOps with Kubeflow
MLOps with Kubeflow MLOps with Kubeflow
MLOps with Kubeflow
Saurabh Kaushik
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
Carl W. Handlin
 
MLOps by Sasha Rosenbaum
MLOps by Sasha RosenbaumMLOps by Sasha Rosenbaum
MLOps by Sasha Rosenbaum
Sasha Rosenbaum
 
MLOps in action
MLOps in actionMLOps in action
MLOps in action
Pieter de Bruin
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale
Databricks
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflow
Databricks
 
MLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLMLOps - The Assembly Line of ML
MLOps - The Assembly Line of ML
Jordan Birdsell
 
How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformHow to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
Databricks
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOps
Databricks
 
ML-Ops how to bring your data science to production
ML-Ops how to bring your data science to productionML-Ops how to bring your data science to production
ML-Ops how to bring your data science to production
Herman Wu
 
MLOps for production-level machine learning
MLOps for production-level machine learningMLOps for production-level machine learning
MLOps for production-level machine learning
cnvrg.io AI OS - Hands-on ML Workshops
 
Apply MLOps at Scale by H&M
Apply MLOps at Scale by H&MApply MLOps at Scale by H&M
Apply MLOps at Scale by H&M
Databricks
 
Ml ops intro session
Ml ops intro sessionMl ops intro session
Ml ops intro session
Avinash Patil
 
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model ServingProductionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model Serving
Databricks
 
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML EngineersIntro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Daniel Zivkovic
 
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleMLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Databricks
 
Mlflow with databricks
Mlflow with databricksMlflow with databricks
Mlflow with databricks
Liangjun Jiang
 

More Related Content

What's hot

MLflow with Databricks
MLflow with DatabricksMLflow with Databricks
MLflow with Databricks
Liangjun Jiang
 
mlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecyclemlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecycle
Databricks
 
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Databricks
 
"Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow""Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow"
Databricks
 
MLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine LearningMLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine Learning
Matei Zaharia
 
MLOps with Kubeflow
MLOps with Kubeflow MLOps with Kubeflow
MLOps with Kubeflow
Saurabh Kaushik
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
Carl W. Handlin
 
MLOps by Sasha Rosenbaum
MLOps by Sasha RosenbaumMLOps by Sasha Rosenbaum
MLOps by Sasha Rosenbaum
Sasha Rosenbaum
 
MLOps in action
MLOps in actionMLOps in action
MLOps in action
Pieter de Bruin
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale
Databricks
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflow
Databricks
 
MLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLMLOps - The Assembly Line of ML
MLOps - The Assembly Line of ML
Jordan Birdsell
 
How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformHow to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
Databricks
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOps
Databricks
 
ML-Ops how to bring your data science to production
ML-Ops how to bring your data science to productionML-Ops how to bring your data science to production
ML-Ops how to bring your data science to production
Herman Wu
 
MLOps for production-level machine learning
MLOps for production-level machine learningMLOps for production-level machine learning
MLOps for production-level machine learning
cnvrg.io AI OS - Hands-on ML Workshops
 
Apply MLOps at Scale by H&M
Apply MLOps at Scale by H&MApply MLOps at Scale by H&M
Apply MLOps at Scale by H&M
Databricks
 
Ml ops intro session
Ml ops intro sessionMl ops intro session
Ml ops intro session
Avinash Patil
 
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model ServingProductionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model Serving
Databricks
 
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML EngineersIntro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Daniel Zivkovic
 

What's hot (20)

MLflow with Databricks
MLflow with DatabricksMLflow with Databricks
MLflow with Databricks
 
mlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecyclemlflow: Accelerating the End-to-End ML lifecycle
mlflow: Accelerating the End-to-End ML lifecycle
 
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
 
"Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow""Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow"
 
MLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine LearningMLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine Learning
 
MLOps with Kubeflow
MLOps with Kubeflow MLOps with Kubeflow
MLOps with Kubeflow
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
 
MLOps by Sasha Rosenbaum
MLOps by Sasha RosenbaumMLOps by Sasha Rosenbaum
MLOps by Sasha Rosenbaum
 
MLOps in action
MLOps in actionMLOps in action
MLOps in action
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflow
 
MLOps - The Assembly Line of ML
MLOps - The Assembly Line of MLMLOps - The Assembly Line of ML
MLOps - The Assembly Line of ML
 
How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformHow to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOps
 
ML-Ops how to bring your data science to production
ML-Ops how to bring your data science to productionML-Ops how to bring your data science to production
ML-Ops how to bring your data science to production
 
MLOps for production-level machine learning
MLOps for production-level machine learningMLOps for production-level machine learning
MLOps for production-level machine learning
 
Apply MLOps at Scale by H&M
Apply MLOps at Scale by H&MApply MLOps at Scale by H&M
Apply MLOps at Scale by H&M
 
Ml ops intro session
Ml ops intro sessionMl ops intro session
Ml ops intro session
 
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model ServingProductionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model Serving
 
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML EngineersIntro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML Engineers
 

Similar to MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani Parkhe and Tomas Nykodym

MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleMLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Databricks
 
Mlflow with databricks
Mlflow with databricksMlflow with databricks
Mlflow with databricks
Liangjun Jiang
 
MLFlow 1.0 Meetup
MLFlow 1.0 Meetup MLFlow 1.0 Meetup
MLFlow 1.0 Meetup
Databricks
 
ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...
ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...
ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...
Amazon Web Services
 
Scaling up Machine Learning Development
Scaling up Machine Learning DevelopmentScaling up Machine Learning Development
Scaling up Machine Learning Development
Matei Zaharia
 
DevBCN Vertex AI - Pipelines for your MLOps workflows
DevBCN Vertex AI - Pipelines for your MLOps workflowsDevBCN Vertex AI - Pipelines for your MLOps workflows
DevBCN Vertex AI - Pipelines for your MLOps workflows
Márton Kodok
 
Nasscom ml ops webinar
Nasscom ml ops webinarNasscom ml ops webinar
Nasscom ml ops webinar
Sameer Mahajan
 
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflows
Márton Kodok
 
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Databricks
 
Building and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache AirflowBuilding and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache Airflow
Kaxil Naik
 
MLOps pipelines using MLFlow - From training to production
MLOps pipelines using MLFlow - From training to productionMLOps pipelines using MLFlow - From training to production
MLOps pipelines using MLFlow - From training to production
Fabian Hadiji
 
Hydrosphere.io for ODSC: Webinar on Kubeflow
Hydrosphere.io for ODSC: Webinar on KubeflowHydrosphere.io for ODSC: Webinar on Kubeflow
Hydrosphere.io for ODSC: Webinar on Kubeflow
Rustem Zakiev
 
Strata parallel m-ml-ops_sept_2017
Strata parallel m-ml-ops_sept_2017Strata parallel m-ml-ops_sept_2017
Strata parallel m-ml-ops_sept_2017
Nisha Talagala
 
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleMLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
Databricks
 
Tech leaders guide to effective building of machine learning products
Tech leaders guide to effective building of machine learning productsTech leaders guide to effective building of machine learning products
Tech leaders guide to effective building of machine learning products
Gianmario Spacagna
 
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and KubeflowKostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
IT Arena
 
Utilisation de MLflow pour le cycle de vie des projet Machine learning
Utilisation de MLflow pour le cycle de vie des projet Machine learningUtilisation de MLflow pour le cycle de vie des projet Machine learning
Utilisation de MLflow pour le cycle de vie des projet Machine learning
Paris Data Engineers !
 
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
MeetupDataScienceRoma
 
databricks ml flow demonstration using automatic features engineering
databricks ml flow demonstration using automatic features engineeringdatabricks ml flow demonstration using automatic features engineering
databricks ml flow demonstration using automatic features engineering
Mohamed MEJDOUBI
 
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...
Sotrender
 

Similar to MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani Parkhe and Tomas Nykodym (20)

MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleMLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
 
Mlflow with databricks
Mlflow with databricksMlflow with databricks
Mlflow with databricks
 
MLFlow 1.0 Meetup
MLFlow 1.0 Meetup MLFlow 1.0 Meetup
MLFlow 1.0 Meetup
 
ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...
ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...
ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle (AIM396-S...
 
Scaling up Machine Learning Development
Scaling up Machine Learning DevelopmentScaling up Machine Learning Development
Scaling up Machine Learning Development
 
DevBCN Vertex AI - Pipelines for your MLOps workflows
DevBCN Vertex AI - Pipelines for your MLOps workflowsDevBCN Vertex AI - Pipelines for your MLOps workflows
DevBCN Vertex AI - Pipelines for your MLOps workflows
 
Nasscom ml ops webinar
Nasscom ml ops webinarNasscom ml ops webinar
Nasscom ml ops webinar
 
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflows
 
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
 
Building and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache AirflowBuilding and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache Airflow
 
MLOps pipelines using MLFlow - From training to production
MLOps pipelines using MLFlow - From training to productionMLOps pipelines using MLFlow - From training to production
MLOps pipelines using MLFlow - From training to production
 
Hydrosphere.io for ODSC: Webinar on Kubeflow
Hydrosphere.io for ODSC: Webinar on KubeflowHydrosphere.io for ODSC: Webinar on Kubeflow
Hydrosphere.io for ODSC: Webinar on Kubeflow
 
Strata parallel m-ml-ops_sept_2017
Strata parallel m-ml-ops_sept_2017Strata parallel m-ml-ops_sept_2017
Strata parallel m-ml-ops_sept_2017
 
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleMLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
MLOps Virtual Event | Building Machine Learning Platforms for the Full Lifecycle
 
Tech leaders guide to effective building of machine learning products
Tech leaders guide to effective building of machine learning productsTech leaders guide to effective building of machine learning products
Tech leaders guide to effective building of machine learning products
 
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and KubeflowKostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
 
Utilisation de MLflow pour le cycle de vie des projet Machine learning
Utilisation de MLflow pour le cycle de vie des projet Machine learningUtilisation de MLflow pour le cycle de vie des projet Machine learning
Utilisation de MLflow pour le cycle de vie des projet Machine learning
 
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform[Giovanni Galloro] How to use machine learning on Google Cloud Platform
[Giovanni Galloro] How to use machine learning on Google Cloud Platform
 
databricks ml flow demonstration using automatic features engineering
databricks ml flow demonstration using automatic features engineeringdatabricks ml flow demonstration using automatic features engineering
databricks ml flow demonstration using automatic features engineering
 
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...
Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...
 

More from Databricks

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
Databricks
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1
Databricks
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
Databricks
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
Databricks
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4
Databricks
 
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Databricks
 
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
Databricks
 
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceLearn to Use Databricks for Data Science
Learn to Use Databricks for Data Science
Databricks
 
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringWhy APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML Monitoring
Databricks
 
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixThe Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Databricks
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Databricks
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Databricks
 
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesScaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Databricks
 
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesScaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Databricks
 
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsSawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature Aggregations
Databricks
 
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkRedis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Databricks
 
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkRe-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and Spark
Databricks
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Databricks
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
Databricks
 
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
Databricks
 

More from Databricks (20)

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1
 
Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4
 
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
 
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
 
Learn to Use Databricks for Data Science
Learn to Use Databricks for Data ScienceLearn to Use Databricks for Data Science
Learn to Use Databricks for Data Science
 
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringWhy APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML Monitoring
 
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixThe Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
 
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesScaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
 
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesScaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
 
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsSawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature Aggregations
 
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkRedis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
 
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkRe-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and Spark
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
 
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
 

Recently uploaded

Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7
Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7
Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7
nitachopra
 
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...
ThinkInnovation
 
Bangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In Bangalore
Bangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In BangaloreBangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In Bangalore
Bangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In Bangalore
yashusingh54876
 
Bangalore ℂall Girl 000000 Bangalore Escorts Service
Bangalore ℂall Girl 000000 Bangalore Escorts ServiceBangalore ℂall Girl 000000 Bangalore Escorts Service
Bangalore ℂall Girl 000000 Bangalore Escorts Service
nhero3888
 
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Timothy Spann
 
🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...
🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...
🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...
yuvishachadda
 
🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...
🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...
🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...
AK47
 
🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...
🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...
🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...
rukmnaikaseen
 
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
9gr6pty
 
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance PaymentCall Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
prijesh mathew
 
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
PsychoTech Services
 
Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)
GeorgiiSteshenko
 
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...
#kalyanmatkaresult #dpboss #kalyanmatka #satta #matka #sattamatka
 
IBM watsonx.data - Seller Enablement Deck.PPTX
IBM watsonx.data - Seller Enablement Deck.PPTXIBM watsonx.data - Seller Enablement Deck.PPTX
IBM watsonx.data - Seller Enablement Deck.PPTX
EbtsamRashed
 
Pune Call Girls <BOOK> 😍 Call Girl Pune Escorts Service
Pune Call Girls <BOOK> 😍 Call Girl Pune Escorts ServicePune Call Girls <BOOK> 😍 Call Girl Pune Escorts Service
Pune Call Girls <BOOK> 😍 Call Girl Pune Escorts Service
vashimk775
 
Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...
Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...
Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...
uthkarshkumar987000
 
Direct Lake Deep Dive slides from Fabric Engineering Roadshow
Direct Lake Deep Dive slides from Fabric Engineering RoadshowDirect Lake Deep Dive slides from Fabric Engineering Roadshow
Direct Lake Deep Dive slides from Fabric Engineering Roadshow
Gabi Münster
 
202406 - Cape Town Snowflake User Group - LLM & RAG.pdf
202406 - Cape Town Snowflake User Group - LLM & RAG.pdf202406 - Cape Town Snowflake User Group - LLM & RAG.pdf
202406 - Cape Town Snowflake User Group - LLM & RAG.pdf
Douglas Day
 
CAP Excel Formulas & Functions July - Copy (4).pdf
CAP Excel Formulas & Functions July - Copy (4).pdfCAP Excel Formulas & Functions July - Copy (4).pdf
CAP Excel Formulas & Functions July - Copy (4).pdf
frp60658
 
Fabric Engineering Deep Dive Keynote from Fabric Engineering Roadshow
Fabric Engineering Deep Dive Keynote from Fabric Engineering RoadshowFabric Engineering Deep Dive Keynote from Fabric Engineering Roadshow
Fabric Engineering Deep Dive Keynote from Fabric Engineering Roadshow
Gabi Münster
 

Recently uploaded (20)

Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7
Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7
Call Girls Goa👉9024918724👉Low Rate Escorts in Goa 💃 Available 24/7
 
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...
 
Bangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In Bangalore
Bangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In BangaloreBangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In Bangalore
Bangalore Call Girls ♠ 9079923931 ♠ Beautiful Call Girls In Bangalore
 
Bangalore ℂall Girl 000000 Bangalore Escorts Service
Bangalore ℂall Girl 000000 Bangalore Escorts ServiceBangalore ℂall Girl 000000 Bangalore Escorts Service
Bangalore ℂall Girl 000000 Bangalore Escorts Service
 
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
 
🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...
🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...
🔥Night Call Girls Pune 💯Call Us 🔝 7014168258 🔝💃Independent Pune Escorts Servi...
 
🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...
🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...
🔥Book Call Girls Lucknow 💯Call Us 🔝 6350257716 🔝💃Independent Lucknow Escorts ...
 
🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...
🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...
🔥College Call Girls Kolkata 💯Call Us 🔝 8094342248 🔝💃Top Class Call Girl Servi...
 
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
 
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance PaymentCall Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
Call Girls Hyderabad ❤️ 7339748667 ❤️ With No Advance Payment
 
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
 
Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)
 
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT ...
 
IBM watsonx.data - Seller Enablement Deck.PPTX
IBM watsonx.data - Seller Enablement Deck.PPTXIBM watsonx.data - Seller Enablement Deck.PPTX
IBM watsonx.data - Seller Enablement Deck.PPTX
 
Pune Call Girls <BOOK> 😍 Call Girl Pune Escorts Service
Pune Call Girls <BOOK> 😍 Call Girl Pune Escorts ServicePune Call Girls <BOOK> 😍 Call Girl Pune Escorts Service
Pune Call Girls <BOOK> 😍 Call Girl Pune Escorts Service
 
Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...
Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...
Independent Call Girls In Bangalore 9024918724 Just CALL ME Book Beautiful Gi...
 
Direct Lake Deep Dive slides from Fabric Engineering Roadshow
Direct Lake Deep Dive slides from Fabric Engineering RoadshowDirect Lake Deep Dive slides from Fabric Engineering Roadshow
Direct Lake Deep Dive slides from Fabric Engineering Roadshow
 
202406 - Cape Town Snowflake User Group - LLM & RAG.pdf
202406 - Cape Town Snowflake User Group - LLM & RAG.pdf202406 - Cape Town Snowflake User Group - LLM & RAG.pdf
202406 - Cape Town Snowflake User Group - LLM & RAG.pdf
 
CAP Excel Formulas & Functions July - Copy (4).pdf
CAP Excel Formulas & Functions July - Copy (4).pdfCAP Excel Formulas & Functions July - Copy (4).pdf
CAP Excel Formulas & Functions July - Copy (4).pdf
 
Fabric Engineering Deep Dive Keynote from Fabric Engineering Roadshow
Fabric Engineering Deep Dive Keynote from Fabric Engineering RoadshowFabric Engineering Deep Dive Keynote from Fabric Engineering Roadshow
Fabric Engineering Deep Dive Keynote from Fabric Engineering Roadshow
 

MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani Parkhe and Tomas Nykodym

  • 1. : Platform for Complete Machine Learning Lifecycle Mani Parkhe & Tomas Nykodym Oct 4, 2018
  • 2. Outline Overview of ML development challenges How MLflow tackles these MLflow components How to get started
  • 4. ML Lifecycle 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 learning platform • Works with any ML library & language • Runs the same way anywhere (e.g. any cloud) • Designed to be useful for 1 or 1000+ person orgs
  • 9. MLflow Components 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. MLflow Tracking Tracking Record and query experiments: code, configs, results, …etc import mlflow with mlflow.start_run(): mlflow.log_param("layers", layers) mlflow.log_param("alpha", alpha) # train model mlflow.log_metric("mse", model.mse()) mlflow.log_artifact("plot", model.plot(test_df)) mlflow.tensorflow.log_model(model)
  • 11. Demo
  • 12. 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. Notebooks Local Apps Cloud Jobs Tracking Server UI API Python or REST API
  • 14.
  • 15. MLflow backend stores 1. Entity Store • FileStore (local and REST) • Database backed (coming soon) 2. Artifact Repository • S3 backed store • Azure Blob storage • Google Cloud storage • DBFS artifact repo
  • 16. Learning More About MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 16
  • 17. Break
  • 18. MLflow Deep Dive Part #2 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 Closer look at MLflow Projects and Models
  • 19. MLflow Deep Dive Part #2 2.1 MLflow Projects • Motivation • Quick Overview • Examples • Demo 2.2 MLflow Models • Motivation • Quick Overview • About Flavors • Examples • Demo Outline
  • 20. 2.1 MLflow Projects • Motivation • Quick Overview • Examples • Demo
  • 21. MLflow Projects Motivation Diverse set of tools Diverse set of environments Result: It is difficult to productionalize and share.
  • 22. Project Spec Code DataConfig Local Execution Remote Execution
  • 23. MLflow Projects Packaging format for reproducible ML runs - Any code folder or Github repository - Optional MLproject file with project configuration Defines dependencies for reproducibility - Conda (+ R, Docker, …) dependencies can be specified in MLproject - Reproducible in (almost) any environment Execution API for running projects locally or remote - CLI / Python / R / Java - mlflow run . -e train … - mlflow.projects.run(., “train”, ...)
  • 24. 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>”, ...)
  • 25. Airbnb Example Revisited We have 2 other data scientists working on the project • Aaron used XGBoost in python • Amy used Keras in R • They provided their code as MLflow project on github Let’s test their code!
  • 26. 2.2 MLflow Models • Motivation • Quick Overview • About Flavors • Examples • Demo
  • 27. Inference Code Batch & Stream Scoring Serving ToolsML Frameworks
  • 28. Model Format Flavor 2Flavor 1 ML Frameworks Inference Code Batch & Stream Scoring Serving Tools Standard for ML models
  • 29. MLflow Models Packaging format for ML Models - Any directory with MLmodel file - Different flavors for different models Defines dependencies for reproducibility - Conda (+ R, Docker, … ?) dependencies can be specified in MLproject - Deployable (almost) anywhere Deployment APIs - CLI / Python / R / Java - mlflow pyfunc serve … - mlflow.pyfunc.load(...).predict()
  • 30. 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 >>> mlflow.tensorflow.log_model(...)
  • 31. About Those Flavors Models can generate several flavors • Spark ML model generates spark, mleap and pyfunc flavors Generic flavors provide abstraction layer • pyfunc can be served locally, as spark_udf, on Azure ML, … • By generating pyfunc flavor we get all above Different Flavors Can be Loaded By Different Languages • pyfunc in python, mleap in Java, crate in R, Keras in python and R
  • 32. PyFunc - Generic Python Model PyFunc is saved as “just a directory”: ./model_dir/ ./MLmodel: configuration <code>: code packaged with the model (specified in the MLmodel file) <data>: data packaged with the model (specified in the MLmodel file) <env>: Conda environment definition (specified in the MLmodel file) Model loader specified in MLmodel file: Arbitrary python code loaded dynamically at runtime Loaded Pyfunc is “just an object” with a predict method: predict(pandas.DataFrame) -> pandas.DataFrame | numpy.array
  • 33. Airbnb Example Revisited (Again) Let’s put our models to production! • Predict from cli • Set up a real time REST API prediction server • Score big data in Spark
  • 34. Advanced MLFlow - HyperParameters 34 HyperParam Search Run Train Model Run mlflow.log_metric()mlflow.get_metric() Projects Tracking Models mlflow run ... Logged Model mlflow.log_artifact
  • 35. Advanced MLFlow - Multistep Workflow
  • 36. Get started with MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 36
  • 37. Ongoing MLflow Roadmap •TensorFlow, Keras, PyTorch, H2O, MLlib integrations ✔ •Java and R language APIs ✔ •Multi-step workflows •Hyperparameter tuning •Data source API based on Spark data sources •Model metadata & management
  • 38. Conclusion Workflow tools can greatly simplify the 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
  • 40. MLflow Design Philosophy 1. “API-first”, open platform • Allow submitting runs, models, etc from any library & language • Example: a “model” can just be a lambda function that MLflow can then deploy in many places (Docker, Azure ML, Spark UDF, …) Key enabler: built around REST APIs and CLI
  • 41. 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 integrate into existing ML platforms & workflows Key enabler: distinct components (Tracking/Projects/Models)
  翻译：