Pythonsevilla2019 - Introduction to MLFlow
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In this talk, I present an introduction of MLFlow. I also show some examples of using it by means of MLFlow Tracking, MLFlow Projects and MLFlow Models. I also used Databricks as an example of remote tracking.
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MLFlow: Platform for Complete Machine Learning Lifecycle
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.
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.
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
Managing the Complete Machine Learning Lifecycle with MLflow
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 solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. 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 the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
MLflow: A Platform for Production Machine Learning
Presentation about MLflow and the ML Platforms / MLOps class of software systems at the NeurIPS 2019 ML Systems workshop.
Introduction to MLflow
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 talk, I 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.
MLOps Using MLflow
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
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
MLOps in action
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Recommended
MLFlow: Platform for Complete Machine Learning Lifecycle
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.
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.
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
Managing the Complete Machine Learning Lifecycle with MLflow
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 solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. 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 the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
MLflow: A Platform for Production Machine Learning
Presentation about MLflow and the ML Platforms / MLOps class of software systems at the NeurIPS 2019 ML Systems workshop.
Introduction to MLflow
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 talk, I 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.
MLOps Using MLflow
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
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
MLOps in action
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
MLOps with Azure DevOps
MLOps refers to applying DevOps practices and principles to machine learning. This allows for machine learning models and projects to be developed and deployed using automated pipelines for continuous integration and delivery. MLOps benefits include making machine learning work reproducible and auditable, enabling validation of models, and providing observability through monitoring of models after deployment. MLOps uses the same development practices as software engineering to ensure quality control for machine learning.
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The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Databricks Overview for MLOps
1) Databricks provides a machine learning platform for MLOps that includes tools for data ingestion, model training, runtime environments, and monitoring.
2) It offers a collaborative data science workspace for data engineers, data scientists, and ML engineers to work together on projects using notebooks.
3) The platform provides end-to-end governance for machine learning including experiment tracking, reproducibility, and model governance.
"Managing the Complete Machine Learning Lifecycle with MLflow"
Machine Learning 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.
Simplifying Model Management with MLflow
<p>Last summer, Databricks launched MLflow, an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs and model packaging. MLflow has grown quickly since then, with over 120 contributors from dozens of companies, including major contributions from R Studio and Microsoft. It has also gained new capabilities such as automatic logging from TensorFlow and Keras, Kubernetes integrations, and a high-level Java API. In this talk, we’ll cover some of the new features that have come to MLflow, and then focus on a major upcoming feature: model management with the MLflow Model Registry. Many organizations face challenges tracking which models are available in the organization and which ones are in production. The MLflow Model Registry provides a centralized database to keep track of these models, share and describe new model versions, and deploy the latest version of a model through APIs. We’ll demonstrate how these features can simplify common ML lifecycle tasks.</p>
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Introducing MLOps.pdf
This talk was jointly organized by BSPIN and ASQ Bengaluru LMC.
It covered the following details-
- Industry 4.0
- Agile , CI/CD, DevOps
- DevOps and MLOps
- Evolution of MLOPS
- MLOps Capabilities
- AI Platform Pipelines
- Training and Tuning AI Platform
- Case Study
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Building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder.
In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 50 contributors and new features including language APIs, integrations with popular ML libraries, and storage backends. I’ll show how MLflow works and explain how to get started with MLflow.
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Ml ops intro session
ML Ops case study document which talks about How ML pipelines are implemented and how to choose and implement Continuous Data 4 ML Pipelines.
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https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and Prometheus
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
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Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Nasscom ml ops webinar
This document outlines a presentation on revamping machine learning pipelines with MLOps. The presentation covers the machine learning lifecycle and challenges with ML productization. It provides examples of end-to-end ML platforms like Uber's Michaelangelo and Google's TFX. The presentation discusses MLOps best practices and methodologies, including build, retrain and release pipelines. It demonstrates MLflow and shows demos of Airflow, Tensorflow model serving, and TFX-based MLOps systems on Google Cloud and Azure.
Convenient Containerization with MLFLow Project
Deploying even regular software applications into production is a difficult task to accomplish. It’s even worse when the application is a Machine Learning pipeline. Usually, Machine Learning models are packaged as standalone, executable entities. However, that presents a lot of problems like platform incompatibility, unscalable models, and inconsistent libraries.
These problems can be solved by deploying Machine Learning models using Docker containers. This has several advantages -
~ Your ML applications can run on any platform without any porting or testing required.
~ Once packaged into containers, ML systems can be exposed as microservices. This allows external services to (containers or not) to leverage them at any time without the need to integrate the code inside the application.
~ ML applications that exist within containers can easily be scaled by placing them on cloud-based systems or on container management systems like Kubernetes or Swarm.
This webinar will walk you through the process of containerizing Machine learning models with MLFlow so that they can be ready for deployment in production environments.
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MLOps refers to applying DevOps practices and principles to machine learning. This allows for machine learning models and projects to be developed and deployed using automated pipelines for continuous integration and delivery. MLOps benefits include making machine learning work reproducible and auditable, enabling validation of models, and providing observability through monitoring of models after deployment. MLOps uses the same development practices as software engineering to ensure quality control for machine learning.
MLOps - The Assembly Line of ML
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Databricks Overview for MLOps
1) Databricks provides a machine learning platform for MLOps that includes tools for data ingestion, model training, runtime environments, and monitoring.
2) It offers a collaborative data science workspace for data engineers, data scientists, and ML engineers to work together on projects using notebooks.
3) The platform provides end-to-end governance for machine learning including experiment tracking, reproducibility, and model governance.
"Managing the Complete Machine Learning Lifecycle with MLflow"
Machine Learning 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.
Simplifying Model Management with MLflow
<p>Last summer, Databricks launched MLflow, an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs and model packaging. MLflow has grown quickly since then, with over 120 contributors from dozens of companies, including major contributions from R Studio and Microsoft. It has also gained new capabilities such as automatic logging from TensorFlow and Keras, Kubernetes integrations, and a high-level Java API. In this talk, we’ll cover some of the new features that have come to MLflow, and then focus on a major upcoming feature: model management with the MLflow Model Registry. Many organizations face challenges tracking which models are available in the organization and which ones are in production. The MLflow Model Registry provides a centralized database to keep track of these models, share and describe new model versions, and deploy the latest version of a model through APIs. We’ll demonstrate how these features can simplify common ML lifecycle tasks.</p>
MLOps with Kubeflow
This document discusses MLOps and Kubeflow. It begins with an introduction to the speaker and defines MLOps as addressing the challenges of independently autoscaling machine learning pipeline stages, choosing different tools for each stage, and seamlessly deploying models across environments. It then introduces Kubeflow as an open source project that uses Kubernetes to minimize MLOps efforts by enabling composability, scalability, and portability of machine learning workloads. The document outlines key MLOps capabilities in Kubeflow like Jupyter notebooks, hyperparameter tuning with Katib, and model serving with KFServing and Seldon Core. It describes the typical machine learning process and how Kubeflow supports experimental and production phases.
Introducing MLOps.pdf
This talk was jointly organized by BSPIN and ASQ Bengaluru LMC.
It covered the following details-
- Industry 4.0
- Agile , CI/CD, DevOps
- DevOps and MLOps
- Evolution of MLOPS
- MLOps Capabilities
- AI Platform Pipelines
- Training and Tuning AI Platform
- Case Study
“Houston, we have a model...” Introduction to MLOps
The document introduces MLOps (Machine Learning Operations) and the need to operationalize machine learning models beyond just model deployment. It discusses challenges like data and model drift, retraining models, software dependencies, monitoring models in production, and the need for automation, testing, and reproducibility across the full machine learning lifecycle from data to deployment. An example MLOps workflow is shown using GitHub and Azure ML to enable experiment tracking, automation, and continuous integration and delivery of models.
mlflow: Accelerating the End-to-End ML lifecycle
Building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder.
In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 50 contributors and new features including language APIs, integrations with popular ML libraries, and storage backends. I’ll show how MLflow works and explain how to get started with MLflow.
Seamless MLOps with Seldon and MLflow
The document provides an overview of seamless MLOps using Seldon and MLflow. It discusses how MLOps is challenging due to the wide range of requirements across the ML lifecycle. MLflow helps with training by allowing experiment tracking and model versioning. Seldon Core helps with deployment by providing servers to containerize models and infrastructure for monitoring, A/B testing, and feedback. The demo shows training models with MLflow, deploying them to Seldon for A/B testing, and collecting feedback to optimize models.
MLflow with Databricks
A basic mlflow introduction and how to work with databricks. The CI/CD workflow with Databricks is also introduced.
Apply MLOps at Scale by H&M
H&M uses machine learning for various use cases including logistics, production, sales, marketing, and design/buying. MLOps principles like model versioning, reproducibility, scalability, and automated training are applied to manage the machine learning lifecycle. The technical stack includes Kubernetes, Docker, Azure Databricks for interactive development, Airflow for automated training, and Seldon for model serving. The goal is to apply MLOps at scale for various prediction scenarios through a continuous integration/continuous delivery pipeline.
From Data Science to MLOps
The document discusses moving from data science to MLOps. It defines MLOps as extending DevOps methodology to include machine learning, data science, and data engineering assets. Key concepts of MLOps include iterative development, automation, continuous integration and delivery, versioning, testing, reproducibility, monitoring, source control, and model/feature stores. MLOps helps address challenges of moving models to production like the deployment gap by establishing best practices and tools for testing, deploying, managing, and monitoring models.
MLOps Virtual Event: Automating ML at Scale
ML is transforming many industries but operating ML systems at scale is complex as it involves many teams, constant data and model updates, and moving from development to production. ML platforms aim to help with this by providing software to manage the entire ML lifecycle from data to experimentation to production deployment through a consistent interface. Desirable features for an ML platform include ease of use, integration with data infrastructure for governance, and collaboration functions to enable sharing of code, data, models and experiments. Databricks provides an open source ML platform that integrates with data lakes and a data science workspace to help organizations perform MLOps at scale.
MLOps by Sasha Rosenbaum
This document discusses MLOps, which is applying DevOps practices and principles to machine learning to enable continuous delivery of ML models. It explains that ML models need continuous improvement through retraining but data scientists currently lack tools for quick iteration, versioning, and deployment. MLOps addresses this by providing ML pipelines, model management, monitoring, and retraining in a reusable workflow similar to how software is developed. Implementing even a basic CI/CD pipeline for ML can help iterate models more quickly than having no pipeline at all. The document encourages building responsible AI through practices like ensuring model performance and addressing bias.
Ml ops intro session
ML Ops case study document which talks about How ML pipelines are implemented and how to choose and implement Continuous Data 4 ML Pipelines.
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate.
MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.
The A-Z of Data: Introduction to MLOps
Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps.
https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and Prometheus
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
MLOps and Data Quality: Deploying Reliable ML Models in Production
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
What's hot (20)
"Managing the Complete Machine Learning Lifecycle with MLflow"
"Managing the Complete Machine Learning Lifecycle with MLflow"
“Houston, we have a model...” Introduction to MLOps
“Houston, we have a model...” Introduction to MLOps
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model Serving
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and Prometheus
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and Prometheus
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in Production
Similar to Pythonsevilla2019 - Introduction to MLFlow
Nasscom ml ops webinar
This document outlines a presentation on revamping machine learning pipelines with MLOps. The presentation covers the machine learning lifecycle and challenges with ML productization. It provides examples of end-to-end ML platforms like Uber's Michaelangelo and Google's TFX. The presentation discusses MLOps best practices and methodologies, including build, retrain and release pipelines. It demonstrates MLflow and shows demos of Airflow, Tensorflow model serving, and TFX-based MLOps systems on Google Cloud and Azure.
Convenient Containerization with MLFLow Project
Deploying even regular software applications into production is a difficult task to accomplish. It’s even worse when the application is a Machine Learning pipeline. Usually, Machine Learning models are packaged as standalone, executable entities. However, that presents a lot of problems like platform incompatibility, unscalable models, and inconsistent libraries.
These problems can be solved by deploying Machine Learning models using Docker containers. This has several advantages -
~ Your ML applications can run on any platform without any porting or testing required.
~ Once packaged into containers, ML systems can be exposed as microservices. This allows external services to (containers or not) to leverage them at any time without the need to integrate the code inside the application.
~ ML applications that exist within containers can easily be scaled by placing them on cloud-based systems or on container management systems like Kubernetes or Swarm.
This webinar will walk you through the process of containerizing Machine learning models with MLFlow so that they can be ready for deployment in production environments.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
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 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.
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Because MLflow is an API-first platform, there are many patterns for using it in complex workflows and integrating it with existing tools. In this talk, we’ll demo a few best practices for using MLflow in a more complex workflow. These include:
* Run multi-step workflows on MLflow, such as data preparation steps followed by training, and organizing your projects so you can automatically reuse past work.
* Tune Hyperparameter on MLflow with open source hyperparameter tuning packages.
* Save a model in MLflow (eg, from a new machine learning library) and deploying it to the existing deployment tools.
MLOps for production-level machine learning
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
Unifying Twitter around a single ML platform - Twitter AI Platform 2019
Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools.
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.
Scaling up Machine Learning Development
An update on the open source machine learning platform, MLflow, given by Matei Zaharia at ScaledML 2020. Details on the new autologging and model registry features, and large scale use cases.
Mlflow with databricks
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
Tuning ML Models: Scaling, Workflows, and Architecture
This document discusses best practices for tuning machine learning models. It covers architectural patterns like single-machine versus distributed training and training one model per group. It also discusses workflows for hyperparameter tuning including setting up full pipelines before tuning, evaluating metrics on validation data, and tracking results for reproducibility. Finally it provides tips for handling code, data, and cluster configurations for distributed hyperparameter tuning and recommends tools to use.
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
How to leverage Azure ML, automated machine learning, and Streamlit to build and test machine learning apps quickly? Find out about our favorite Hackathon stack and walk away with some code to build and user-test your own machine learning ideas fast.
Experimentation, bringing machine learning ideas in front of users, is essential to innovation. Yet, in our corporate hackathons, our data science team has struggled many times with how to build and deploy user-facing machine learning ideas in just a single day.
Over the past 2+ years, we have developed a routine around using Azure Machine Learning, automated machine learning, and Streamlit to build and user test machine learning ideas quickly. The aim of this talk is to pass on practical, technical knowledge to fellow data scientists about how to leverage this stack to achieve high build and user test speeds.
During the talk, we will walk through the process of building a computer vision system for identifying trash in images via an app using the open-source TACO dataset (http://tacodataset.org/). Working through a Jupyter notebook, we will load the data into Azure Machine Learning and trigger an automated machine learning run on the data. In this context, we will quickly get to know the training and testing metrics available in Azure ML to evaluate the model. We will then download the machine learning model as a file packaged in the open-source ONNX format (https://onnx.ai/). Using the open-source Python web application framework Streamlit (https://github.com/streamlit/streamlit), we will program an application in which users can upload images and embed the machine learning model in it to identify trash in these images. Using a to-be-published infrastructure-as-code pipeline on Azure DevOps, we will deploy the application to the public internet on the Azure platform. From here, users can test it.
The stack and code presented in this talk will enable fellow data scientists to accelerate their data science development, leading to quicker experimentation and, therefore, to faster innovation of products with machine learning at their core.
Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific
Thermo Fisher Scientific has one of the most extensive product portfolios in the industry, ranging from reagents to capital instruments across customers in biotechnology, pharmaceuticals, academic, and more.
Open, Secure & Transparent AI Pipelines
AI algorithms offer great promise in criminal justice, credit scoring, hiring and other domains. However, algorithmic fairness is a legitimate concern. Possible bias and adversarial contamination can come from training data, inappropriate data handling/model selection or incorrect algorithm design. This talk discusses how to build an open, transparent, secure and fair pipeline that fully integrates into the AI lifecycle — leveraging open-source projects such as AI Fairness 360 (AIF360), Adversarial Robustness Toolbox (ART), the Fabric for Deep Learning (FfDL) and the Model Asset eXchange (MAX).
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
ML solutions in production start from data ingestion and extend upto the actual deployment step. We want this workflow to be scalable, portable and simple. Containers and kubernetes are great at the former two but not the latter if you aren't a devops practitioner. We'll explore how you can leverage the Kubeflow project to deploy best-of-breed open-source systems for ML to diverse infrastructures.
Databricks for MLOps Presentation (AI/ML)
In this session, we will be introducing how we can utilize Databricks to achieve MLflow in Machine learning. The main highlight for this session will be featured in machine learning like MLflow with Databricks for every experiment tracking, how we can do model packaging, and how we can deploy the model of machine learning in Databricks.
TensorFlow 101
This document provides an overview of Machine Learning with TensorFlow 101. It introduces TensorFlow, describing its programming model and how it uses computational graphs for distributed execution. It then gives a simplified view of machine learning, and provides examples of linear regression and deep learning with TensorFlow. The presenter is an entrepreneur who has been dabbling with machine learning for the past 3 years using tools like Spark, H2O.ai and TensorFlow.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
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.
Dmitry Spodarets: Modern MLOps toolchain 2023
Dmitry Spodarets: Modern MLOps toolchain 2023
AI & BigData Online Day 2023 Spring
Website - www.aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Team Data Science Process Presentation (TDSP), Aug 29, 2017
Microsoft Team Data Science Process (TDSP) Presentation at 5th Global Big Data Conference, Santa Clara, Aug 29, 2017
Similar to Pythonsevilla2019 - Introduction to MLFlow (20)
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Unifying Twitter around a single ML platform - Twitter AI Platform 2019
Unifying Twitter around a single ML platform - Twitter AI Platform 2019
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
Tuning ML Models: Scaling, Workflows, and Architecture
Tuning ML Models: Scaling, Workflows, and Architecture
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific
Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Team Data Science Process Presentation (TDSP), Aug 29, 2017
Team Data Science Process Presentation (TDSP), Aug 29, 2017
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HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
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This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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Pythonsevilla2019 - Introduction to MLFlow
- 1. Introduction to MLFLOW Accelerating the Machine Learning Lifecycle
- 2. Who am I? Fernando Ortega Gallego Data Enginer @ Plain Concepts UK • Interests & hobbies: – Machine Learning and NLP – Python, Azure and my daughter – Honda biker @FernanOrtega fgallego@plainconcepts.com
- 3. Roadmap Introduction MLFlow Tracking MLFlow Projects MLFlow Models Conclusions
- 4. Roadmap Introduction MLFlow Tracking MLFlow Projects MLFlow Models Conclusions
- 6. Problems • Reproduce experiments • Compare experiments • Fine tune previous experiments across teams • Share data, parameters or metrics • Deploy trained models 7
- 7. It is difficult to productionize and share 8
- 8. Custom ML Platforms • Facebook FBLearner, Google TFX, Uber Michelangelo • Advantages: – Standarise the ML loop • Disadvantages: – Limited to a few algorithms or frameworks – Tied to the company’s infrastructure 9
- 9. Are there any similar solutions in an open manner? 10
- 10. 11
- 11. Introducing MLFlow • It works with any ML library & language • It runs the same way anywhere • It is designed to be useful both for 1 person, small teams or big teams
- 14. MLFlow components 15 Tracking Projects Models
- 15. Roadmap Introduction MLFlow Tracking MLFlow Projects MLFlow Models Conclusions
- 16. MLFlow Tracking • Record and query experiments: – Data – Code – Model parameters – Results (performance metrics) – Model 17 Tracking
- 17. Motivation 18 Centralised repository of useful information to analyse several runs of training.
- 18. How it is working 19
- 19. Key concepts • Parameters • Metrics • Tags and notes • Artifacts • Source • Version 20
- 20. Code example 21
- 21. 22 Demo
- 22. Roadmap Introduction MLFlow Tracking MLFlow Projects MLFlow Models Conclusions
- 23. MLFlow Projects • Packaging format for reproducible ML runs • Defines dependencies for reproducibility • Execution API for running projects locally or remote 24 Projects
- 24. Motivation 25 Diverse set of tools and environments involves difficulty to productionalize and share ML work
- 25. How it is working 26
- 26. Key concepts • MLproject file • Entry points • Environments – Conda – Docker – System • Run 27
- 27. Code example 28 mlflow run git@github.com:mlflow/mlflow-example.git -P alpha=0.5 mlflow run <uri> -m databricks --cluster-spec <json-cluster-spec>
- 28. 29 Demo
- 29. Roadmap Introduction MLFlow Tracking MLFlow Projects MLFlow Models Conclusions
- 30. MLFlow Models • Packaging format for ML Models • Defines dependencies for reproducibility • Deployment APIs 31 Models
- 31. Motivation 32 ML Frameworks Batch & Stream Scoring Serving tools Inference code
- 32. How it is working 33
- 33. Key concepts • MLmodel file • Storage format • Entry points • Flavours • Custom model 34
- 34. Code example 35
- 35. 36 Demo
- 36. Roadmap Introduction MLFlow Tracking MLFlow Projects MLFlow Models Conclusions
- 37. MLFlow rocks! • Log important parameters, metrics, and other data that is important to the machine learning model • Track the environment a model is run on • Run any machine learning codes on that environment • Deploy and export models to various platforms with multiple packaging formats 38
- 38. MLFlow 1.0 (4-jun) • Support for step tracking • Improved Search features • Batched logging of metrics • Support for HDFS • Windows support for the client • Build Docker images to deploy • ONNX model flavour 39
- 39. MLFlow last updates (1.4: 31-oct) • Windows support • Tags and descriptions • Google Cloud run models • Log directories as artifacts • CLI command to export to CSV • Keras compatibility with TF 2.0 • Model Registry in preview 40
Editor's Notes
- Backend stores are entity store (filestore and database) and artifact repository (s3, azure blob, Google cloud, dbfs)
- Parameters: key-value inputs to your code Metrics: numeric values Tags and notes: additional information about a run Artifacts: arbitrary files, including data and models Source: code Version: version of the code
- Backend stores are entity store (filestore and database) and artifact repository (s3, azure blob, Google cloud, dbfs)
- Backend stores are entity store (filestore and database) and artifact repository (s3, azure blob, Google cloud, dbfs)
- 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