MLOps: Building Continuous Training and Delivery Pipelines

MLOps is an emerging engineering movement aimed at accelerating the delivery of reliable, working ML software on an ongoing basis. This post provides an intro to MLOps and gives you an example project to get you started with building your own ML pipelines using GitHub Actions and Google Cloud.

MLOps: Building Continuous Training and Delivery Pipelines

What you'll learn

This post aims to help you get started with building robust, automated ML pipelines (on a budget!) for automatically retraining, tracking and redeploying your models. It covers:

  • An overview of the origins and aims of the MLOps movement;
  • An introduction to a couple of key MLOps concepts;
  • A tutorial for setting up a Continuous Training/Continuous Delivery (CT/CD) ML pipeline with GitHub Actions and Google Cloud Functions.

The tutorial section is designed to make use of free (or nearly free) services, so following along should cost you a few pennies at most. If you're working on an MVP and need some ML infrastructure in place sharpish but want to avoid the price tag and technical overhead of AWS SageMaker or Azure ML deployments, you might find the example useful too. Finally, if you're interested in understanding how the tutorial fits together to run it end-to-end for yourself, you should check out the previous post in this series on deploying lightweight ML models as serverless functions.

As always, for those of you wanting to get stuck in to the code, check out the GitHub workflows file (.github/workflows/pipeline.yml) in here:

A repository providing demo code for deploying a lightweight Scikit-Learn based ML pipeline modelling heart disease data as a Google Cloud Function. - markdouthwaite/serverless-scikit-learn-demo

What is MLOps?

In the last decade or so, the movement popularly referred to as 'DevOps' has gained a significant professional following within the world of software engineering, with a large number of dedicated DevOps roles springing up across development teams around the world. The motivation for this movement is to combine aspects of software development (Dev) with elements of Operational (Ops) software activities with the aim of accelerating the delivery of reliable, working software on an ongoing basis.

A major focus for adherents of the DevOps movement is on establishing and maintaining Continuous Integration and Continuous Delivery (CI/CD) pipelines. In practice, well designed and cleanly implemented CI/CD pipelines offer teams utilising them the ability to continuously modify their software system to (in principle) dramatically reduce the time-to-value for new software patches and features, while simultaneously minimising the risk of downside from bugs and outages related to releasing these patches and features. Teams operating mature implementations of this delivery mechanism often release updates on an hourly basis (or faster!) with the ability to quickly and cleanly rollback changes if they introduce a bug (though most of these should be caught somewhere in the pipeline).

In contrast, 'traditional' approaches to releasing software essentially stockpile fixes and features for predefined release windows (perhaps on a weekly, monthly or quarterly basis). While this sort of approach is not uniformly a poor approach, it does introduce a lot of pressure around the release window, can create a lot of complexity around the product integration and release process, and ultimately heighten the risk of serious service outages and by extension brand damage.

How does this relate to machine learning?

At this point, you may well be thinking 'this is all well and good, but how does this relate to machine learning?'. Much like successful commercial software products, successful commercial Machine Learning (ML) projects require that the users of the ML service trust the validity and stability of the service they consume. Importantly, this must be on an ongoing basis – potentially for years at a time.

Consequently, ad hoc model training and evaluation activities that require significant manual intervention akin to 'traditional' software delivery processes on the part of Data Science teams – an all too common sight in the world of Data Science – can introduce serious technical and business risk into a service's lifecycle, and should be thought of as particularly pernicious form of technical debt.

Much like successful commercial software products, successful commercial Machine Learning (ML) projects require that the users of the ML service trust the validity and stability of the service they consume.

That is where MLOps comes in. This nascent movement can be regarded as a superset of the DevOps movement: it aims to accelerate the delivery of reliable, working ML software on an ongoing basis. It therefore concerns itself with CI/CD pipelines in much the same way as DevOps, but also adds specific variations on these CI/CD problems. Most notably, the concept of Continuous Training (CT) is added to the mix. What is CT, you ask?

  • Continuous Training - Many ML services require their underlying ML models to be retrained on some fixed basis, or on the occurence of specific events (e.g. data change, upstream model update). Additionally, it is important that these newly retrained models conform to the underlying assumptions and behaviours that defined the previous version of the model in question. Continuous Training, then, is about establishing robust, automated processes for training and deploying models as a specialised variation of conventional Continuous Delivery pipelines, including the pre-processing, evaluation, selection and serving of ML models as part of broader software services.

The aim here is to allow ML practitioners to quickly and confidently deploy their latest and greatest models into production, while preserving the stability of the services that rely upon them.

Getting started

The ML world is increasingly recognising the need for teams that are capable of autonomously developing and deploying 'production grade' ML solutions – particularly in product-focused ML applications – and by extension the potential technical (and business) risks of having organisationally isolated ML/technical capabilities. In many ways, this is convergent with the learnings of the broader software engineering community in recognising similar needs (and risks) for 'conventional' software applications.

As with the broader software engineering ecosystem before it, the ML toolchain is becoming commoditised: the barriers to entry (cost, expertise) are gradually being lowered and the market is becoming more competitive.

This recognition has resulted in a burgeoning ecosystem around many of the problems outlined above. Each passing month sees the introduction of new tools and platforms explicitly targeted at ML/MLOps challenges. As with the broader software engineering ecosystem before it, the ML toolchain is becoming commoditised: the barriers to entry (cost, expertise) are gradually being lowered and the market is becoming more competitive. For those of you looking to get started with MLOps (or ML in general), this makes it a very exciting time to get involved: you can go a long way towards having 'production ready' ML pipelines for very little cost. Here's how.

Creating an ML pipeline

Right, it is about time for the tutorial!

The ML pipeline presented here is built using GitHub Actions - GitHub's Workflow automation tool. If you sign up for a GitHub account, you get access to this feature for free. There are usage and resource limits (as you might expect), but these are surprisingly generous as a free offering. If you can keep your models relatively lightweight, you could build a small product on top of the free offering alone, so for those of you with some cool MVP ideas, it could be a great place to start. Plus, if the free resources aren't cutting it for you, you can also create 'self hosted' actions too.

So what will the pipeline example presented here do? The pipeline is going to be a basic CT/CD pipeline built on top of the Serverless ML example discussed in a previous post. Concretely, the pipeline will:

  1. Setup your environment and install dependencies.
  2. Download the latest available version of a dataset.
  3. Train and evaluate a new version of a model on the latest dataset.
  4. Upload the new model and evaluation results.
  5. Trigger the redeployment of your new model as an API if the previous steps succeed.

Additionally, you're going to see how you can schedule this workflow to run on a regular basis as a cron job (again using GitHub Actions).

Before you begin

If you'd like to run the pipeline (and the code example), you'll need to register for a Google Cloud account. At the time of writing, you'll get $300 of credit added to your account. That'll more than cover the costs of running the code in this tutorial (which will be essentially free, anyway!). You can find out more here:

GCP Free Tier - Free Extended Trials and Always Free | Google Cloud
GCP Free Tier expands our free program in two ways. $300 credit in a 12-month free trial and Always Free. Learn more.

Next, you should create a fork of the repository into your personal GitHub account. With this done, you'll need to set up a couple of GitHub secrets to allow your pipeline to access your Google Cloud services. Specifically, you'll need to add:

Note: As always with confidential/security info, don't share your SA key with anyone! Ideally, delete it when you're done with this example (unless you want to continue using it for your own projects of course).

With those set up, you'll need to enable your Google Cloud Build, Cloud Functions and Cloud Storage APIs. To do this, simply look through the left-hand navigation bar in the Google Cloud Console and select the relevant cloud services. If the relevant API is not activated, you'll be given the option to activate it when you've clicked on the service.

Finally, in the pipeline definition below, you'll need to define a unique bucket name in your Google Cloud Storage. The name used in the example pipeline definition (below) is pipeline-example. You should replace this with the name of your own bucket after forking but before trying to run the example. Additionally, you'll want to upload the datasets/default.csv dataset in the repository to {your-bucket-name}/heart-disease/dataset.csv.

Now, for good or ill, it is time for some YAML – The Language of the Cloud.

Pipeline definition

The pipeline is defined as a YAML file. You should check out GitHub's introduction to the workflow file format too, if you feel like being particularly thorough. If you're comfortable with YAML, this is probably easy enough to follow, but either way, here's how it breaks down:

0. Job setup & scheduling

This section of the file names the workflow Train and Deploy (this will be how GitHub displays your workflow in the GitHub UI), and then provides event triggers that will trigger your pipeline to run. In this case, you'll see that there are two triggers: push and schedule.

In the case of push, the pipeline will run every time you update your repository's master branch (the specific branches can be listed under the branches field). Practically, this means every time you merge a change to the code in the repository into master the pipeline will retrain the model and redeploy it. This can be useful for immediately propagating code changes to a live ML service.

For the schedule trigger, you simply set a cron schedule for your pipeline to run on. In other words: a fixed schedule on which your pipeline will run. The example value provided below will run the pipeline every weekday morning at 08:00. If you're not sure how to configure a cron schedule, here's a great interactive editor for you to play with.

name: Train and Deploy

      - master
    - cron:  '0 8 * * MON-FRI'

Feel free to play with other triggers. Additionally, you can write custom triggers too, if you're feeling fancy. These custom triggers could help you setup a 'true' event-driven architecture. Just a thought.

1. Environment setup

Now for the boring, but very important step. The key train defines the name of the step. Here you have a single job. Within this, you must define the virtual machine you're going to run your job on (runs-on) . In this case, the example is using ubuntu-latest. Next you define each of the steps within the job.

First, the actions/checkout@v2 is run. This runs the GitHub-provided checkout action. This'll clone and checkout the default (typically master) branch of your repository.

Next, the job sets up the gcloud command line tool. This step uses an action provided by Google Cloud to make your life that little bit easier. This allows subsequent steps to access the gcloud and gsutils command line tools. You'll use these later too download/upload data from/to Google Cloud, and to redeploy your model API.

After this, you have two Python related steps. The first sets up a basic Python 3.7 environment. The second installs any dependencies you have in the toplevel requirements.txt file. And with that, your job is configured to run your pipeline proper. Now for the fun bits.

    runs-on: ubuntu-latest
    - uses: actions/checkout@v2

    - name: Setup GCP client
      uses: GoogleCloudPlatform/github-actions/setup-gcloud@master
        version: '290.0.1'
        project_id: ${{ secrets.GCP_PROJECT_ID }}
        service_account_key: ${{ secrets.GCP_SA_KEY }}
        export_default_credentials: true
    - name: Set up Python
      uses: actions/setup-python@v2
        python-version: 3.7
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

Note that the run key lets you execute commands in the virtual machine's shell – in this case the default Ubuntu shell.

2. Download dataset

First up, the job downloads the latest available dataset from the pipeline-example bucket. In practice, the pipeline would pick up the latest version of the dataset available on the provided path. This allows you to create an independent data pipeline to load and transform the dataset for the ML pipeline to pick up when it next runs.

    - name: Download the latest dataset
      run: |
        gsutil cp gs://pipeline-example/heart-disease/dataset.csv datasets/default.csv

You'll see that the step uses gsutil, Google's Cloud Storage command line utility. This lets you copy files to and from Google Cloud. Simple!

3. Train and evaluate model

Now the job has loaded the latest dataset, it is time to run the 'core' training task. In this case, this is identical to the example given in the previous Serverless ML tutorial.

    - name: Run training task
      run: |
        python steps/ --path=datasets/default.csv

As a bonus, the steps/ script writes the following metadata and metrics to the artifacts/metrics.json path. As you'll see, this gets uploaded to Google Cloud too, so you can review how model performance (and training duration) changes over time. This can come in very handy!

metrics = dict(
    elapsed = end - start,
    acc = acc,
    val_acc = val_acc,
    roc_auc = roc_auc,
    timestamp =

The newly trained model is written to artifacts/pipeline.joblib. This'll be uploaded to Google Cloud too for archiving purposes.

4. Upload model and metrics

The next step is to push your new model and metrics to Google Cloud Storage. You'll see that the pipeline uploads three files:

  • latest.joblib - The 'latest' version of the model. This will be the most current 'valid' model the pipeline has produced.
  • ${{ env.GITHUB_RUN_ID }}.joblib - An archived version of the above model (identified by the unique GitHub run ID that produced it).
  • metrics/${{ env.GITHUB_RUN_ID }}.json - An archived version of the above model's metrics (identified by the unique GitHub run ID that produced it). These can be ordered by the date they were created to produce a time series showing model performance over time.
    - name: Upload new model and associated metrics
      run: |
        gsutil cp artifacts/pipeline.joblib gs://pipeline-example/heart-disease/models/latest.joblib
        gsutil cp artifacts/pipeline.joblib gs://pipeline-example/heart-disease/models/${{ env.GITHUB_RUN_ID }}.joblib
        gsutil cp artifacts/metrics.json gs://pipeline-example/heart-disease/models/metrics/${{ env.GITHUB_RUN_ID }}.joblib

Now you have a new model, some metrics and all of it neatly archived in Google Cloud.

5. Redeploy Cloud Function

With all that done, it is time to redeploy your latest model.

- name: Deploy model as Cloud Function
      run: | 
        gcloud functions deploy heart-disease --entry-point=predict_handler --runtime=python37 --project=${{ secrets.GCP_PROJECT_ID }} --allow-unauthenticated --trigger-http

This will trigger your model's Cloud Function to be redeployed with your latest model. If you want to understand what the arguments passed to gcloud do, make sure to check out the previous post in this series. After a few moments, your new model will be rotated into service.

Finishing up

And that's it. If your pipeline completes successfully, you'll be able to see a nice long list of green ticks for each successful step.

You've now got an automated pipeline for loading new data, retraining a model, archiving the new model and its metrics and then redeploying your model API on a fixed schedule. Pretty cool, eh?

Next steps

This is only a baby ML pipeline. There's a lot things you could do to make it more sophisticated. Some initial ideas could be:

  • Create a Cloud Function that is triggered when model metrics are uploaded to notify you (maybe via Slack or email) if model performance drops below a given value.
  • Add a step to abort model redeployment if evaluation results are worse than previous model (or some test cases).
  • Add a custom Cloud Function trigger to run the workflow when your dataset updates (rather than on a fixed basis, which may be unnecessary).
  • Add a StreamLit dashboard for visualising models and metrics over time.

If you have any issues getting the example working, feel free to get in touch!

Further reading

If you're interested in learning more about MLOps, here's some links you may find interesting: