Category: Mlflow demo

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mlflow demo

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. We're going to demo the three core components that make up mlflow:. Note : you don't have to use all three, each feature can be used independently. This allows us to log all aspects of the ML process - like different hyperparameters we tried, evaluation metricsas well as the code we ran - alongside other arbitrary artifacts such as test data.

This also provides a leaderboard-style UI that makes it easy to see which model performed the best. These are all about reproducibility and sharing. We can also export them to a standard format that can be deployed to any number of systems.

Since most deployment systems use some sort of container based solution e. AzureML or Sagemakermodels make easy deployments to these systems - or we can deploy directly to Kubernetes or Azure Container Registry.

Getting started with MLFlow in Databricks

In this case, we'll be using the " Inside Airbnb " dataset, and loading it from a csv from an Azure Storage Container. We perform multiple experiments using scikit-learn's Random Forest Regressor and log the models on MLflow to demonstrate the tracking capabilities.

We will also load and run a Project straight from git to demonstrate git integration capabilities. We explore the power of model flavors and framework abstraction capabilities available with MLflow models. We will build a Docker Container Image for a trained model and deploy to Azure Container Instance can easily swap to Kubernetes as well. Skip to content.

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Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. MLflow end-to-end demo tracking, projects, model with Azure Databricks. HTML Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

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Latest commit Fetching latest commit…. Primary components We're going to demo the three core components that make up mlflow: Note : you don't have to use all three, each feature can be used independently.

Tracking This allows us to log all aspects of the ML process - like different hyperparameters we tried, evaluation metricsas well as the code we ran - alongside other arbitrary artifacts such as test data. Projects These are all about reproducibility and sharing. Agenda In this notebook we will demonstrate the following topics: Step 1: Load our exploration dataset into a DataFrame In this case, we'll be using the " Inside Airbnb " dataset, and loading it from a csv from an Azure Storage Container.

Step 2: Perform basic exploratory analysis Like plotting on a heatmap to get a better sense of the data. Step 5: Model Management Demo: Explore model flavors and framework abstraction capabilities We explore the power of model flavors and framework abstraction capabilities available with MLflow models.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.Selling something can be hard work. A sales team has to sort through a long list of potential customers and figure out how to spend their time. This is a system that analyzes attributes about each new lead in relation to the chances of that lead actually becoming a customer, and uses that analysis to score and rank all of the potential customers.

mlflow demo

With that new ranking, the sales team can then prioritize their time, and only spend time on the leads that are highly likely to become paying customers. Cool, that sounds great! How do I do it? In this post, we will walk through the full end-to-end implementation of a custom built lead-scoring model. This includes pulling the data, building the model, deploying that model, and finally pushing those results directly to where they matter most — the tools that a sales team uses.

If you want to test out this model without going through the full process, we have a fully-functioning lead scoring model on Booklet. This will be a technical tutorial that requires a bit of coding and data science understanding to get through. To get the most out of this, you should have at least a bit of exposure to:. Also, you should have a few things installed to make sure you can move quickly through the tutorial:. An AWS username with access through awscli we will cover this below!

Python 3 of some kind with a few packages:. There are a few tools that we will be using:.

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Here is a diagram that outlines how these different tools are used:. At the highest level, we will use a Jupyter notebook to pull leads data and train a model.

Getting started with mlFlow

Next, we will send that model to MLflow to keep track of the model version. Then, we will send both a docker container and the model into AWS Sagemaker to deploy the model. Finally, we will use Booklet to put that model to use and start piping lead scores into Intercom.

First, we need to access data about our leads. This data should have two types of information:. A The response variable: Whether or not the lead converted into a paying customer.

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B The features: Details about each lead that will help us the response variable. For this exercise, we are going to use an example leads dataset from Kaggle. This dataset provides a large list of simulated leads for a company called X Education, which sells online courses. We have a variety of features for each lead as well as whether or not that lead converted into a paying customer.This means that it has components to monitor your model during training and running, ability to store models, load the model in production code and create a pipeline.

Tracking is maybe the most interesting feature of the framework. It allows you to create an extensive logging framework around your model. You can define custom metrics so that after a run you can compare the output to previous runs. We will mainly focus on this part but also give you a peek into the other features. This feature allows you to create a pipeline if you so desire. This feature uses its own template to define how you want to run the model on a cloud environment.

As most companies have a way to run code in production this feature might be of less interest to you. Finally we have the Models feature. An mlFlow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.

First we will need to spin up a mlFlow server before we can actually start. To do this properly I created a docker container for ease of deployment. Before we show the code it is also important to configure the storage backend.

As we want our model to be stored somewhere I have chosen Azure blob storage please note that AWS S3 is also supported. So create a blob storage account and inside create a container. Once it has been created you will need to write the wasb link down as you will need this value for starting up docker.

Next we can start building the docker. As mlFlow requires python I made my life a bit easier by starting from a python image. Basically you can start from any image as long as you make sure Python is available in the container. The only 2 packages you need to install for this example are the following:.This means that it has components to monitor your model during training and running, ability to store models, load the model in production code and create a pipeline.

Tracking is maybe the most interesting feature of the framework. It allows you to create an extensive logging framework around your model. You can define custom metrics so that after a run you can compare the output to previous runs. We will mainly focus on this part but also give you a peek into the other features. This feature allows you to create a pipeline if you so desire. This feature uses its own template to define how you want to run the model on a cloud environment.

As most companies have a way to run code in production this feature might be of less interest to you. Finally we have the Models feature. An mlFlow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark. First we will need to spin up a mlFlow server before we can actually start.

To do this properly I created a docker container for ease of deployment. Before we show the code it is also important to configure the storage backend. As we want our model to be stored somewhere I have chosen Azure blob storage please note that AWS S3 is also supported.

So create a blob storage account and inside create a container. Once it has been created you will need to write the wasb link down as you will need this value for starting up docker.

Next we can start building the docker. As mlFlow requires python I made my life a bit easier by starting from a python image. Basically you can start from any image as long as you make sure Python is available in the container. The only 2 packages you need to install for this example are the following:.

If all is well your homepage might look something like this. Once the server part is ready it is time to adapt our code. Before you can start we will need to define the URL on which the server is running. This has been hardcoded for this demo but ideally this would point to a public endpoint.Your company e.

You begin by building a basic machine learning pipeline for a single country in a Jupyter notebook. The evaluation metrics for your basic, single-country pipeline looks good. Next, you want to apply the same pipeline to the other countries—since the data format is identical—and run multiple experiments e. You also want to store and access artifacts e. One notebook per country. However, as you experiment, you find some new features that improve results and want to replicate it across the countries.

Thus, you copy-paste code across multiple notebooks. This violates the DRY principle and is pretty tedious. To log the results in a single location, you output evaluation metrics for each experiment in a CSV. Visualizations e.

However, trying to match the visualizations in the directory to the experiment results in the CSV for reference is time-consuming. Ditto for the model binary. No more duplication of notebooks. All metrics, visualizations, and model binaries in a single UI. In this notebook we have a basic pipeline doing some analysis, visualizations, feature engineering, and machine learning. At a high level, it:. The pipeline is simple—running it end-to-end takes 3. This allows for rapid, iterative experimentations.

Papermill allows you to parametrise and execute Jupyter notebooks. Each experiment is also saved to its own notebook e.

Using papermill to do this is easy—just two simple steps. First, add a parameters tag to the cell in your notebook:. Next, we create a notebook runner. With this approach, we minimize code duplication. Nonetheless, how can we get an overall view of experiment results, without going through each notebook?

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Can this then be consolidated and accessed from a single location? Each notebook trains five ML models and evaluates them on four metrics. For each model, we produce two graphs i. To get this under control, we can use mlflow. MLflow is a framework that helps with tracking experiments and ensuring reproducible workflows for deployment.

It has three components tracking, projects, models. This walkthrough will focus on the first which has an API and UI for logging parameters, metrics, artifacts, etc.

In the above, with every ML model trained, we log parameters e.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Stable functionality are designated by tags, e.

This repo demonstrates the use of the open source project mlflowwhich is used to record and manage results of machine learning experiments. Docker containers provide the run-time environment for this demonstration.

Setup described in this section does not consider security requirements and is suitable only for demonstration purposes with non-sensitive data. Changes are required for any production deployment. Web-based Postgres Administration Tool. To disable the database tracker components, open docker-compose. Skip the next step. To make use of the database tracker components, run the following command to pull PostgresSQL database related images from dockerhub.

After building the Docker images, navigate to. Ensure the required environment variables are defined by running. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Demonstrates mlflow functionality. Docker containers provide the run-time environment for mlflow. Jupyter Notebook Other. Jupyter Notebook Branch: master. Find file. Sign in Sign up.

Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Set up local storage Clone repo to local computer.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

mlflow demo

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A demo project using MLflow machine learning lifecycle management platform. We use here a public Kaggle dataset, and we're building an ML model for predicting a pulsar star.

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You can find the data and its description here. You can create a directory named data at the root of your project and download the contents of the CSV in it, the unzip it. To run this project, it is recommended to first setup a virtual environment and install the requirements. Now that we've built our local mlflow server, we can write a minimal training script for our ml model.

Here we build a very simple model with Scikit Learn. The goal is not to spend time on model optimization, but rather deploy a working model quickly. We will still have the opportunity to enhance it once a first version is developed.

However, a very important concept here is to always use pipelines to train ml models. The concept of pipelines is present in almost every ml library. It allows easier deployment. In this section, we will build a Docker container exposing the mlflow-tracking server api. It will allow us to update our training script in order to log metrics and models in mlflow.

Now that we have a working mlflow server, we can use it to export both our model and our training metrics. Then we update the code of our training script to export the model metrics and pipeline to mlflow:. NB: right now, there is a bug when trying to display the artifacts stored in mlflow ui if you click on a run.

This bug is not present in a production setup.

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Now that we've trained a model, we're ready to deploy it and make it available throught a REST api. For that we will use MLflow models utils. First, let's get the location of our model. It is printed to the console at the end of the training script. You can copy it manually and export it as an environment variable:. After the magic happens, we can now test our model by sending an HTTP request to the deployed endpoint:. In this section we'll see how to use the mlflow projects command line to run our code, and how to make it runnable via this command.

We will no longer use a local version of the mlflow tracking server, but a remote one that we've deployed on google cloud infrastructure.


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