The Full Stack 7-Steps MLOps Framework

Learn MLE & MLOps for free by designing, building, deploying and monitoring an end-to-end ML batch system | source code + 2.5 hours of reading & video materials on Medium

This repository contains a 7-lesson FREE course to teach you how to build a production-ready ML batch system. Its primary focus is to engineer a scalable system using MLOps good practices. You will implement an ML system for forecasting hourly energy consumption levels across Denmark.

You will learn how to build, train, serve, and monitor an ML system using a batch architecture. We will show you how to integrate an experiment tracker, a model registry, a feature store, Docker, Airflow, GitHub Actions and more!

Level: Intermediate to Advanced | This course targets MLEs who want to build end-to-end ML systems and SWEs who wish to transition to MLE.

Following the documentation on GitHub and the lessons on Medium, you have 2.5 hours of reading & video materials, which will help you understand every piece of the code!

At the end of the course, you will know how to build everything from the diagram below 👇

Don't worry if something doesn't make sense to you. We will explain everything in detail in the Medium lessons.

If you are unsure if this course is for you, here is an article presenting a high-level overview of all the components you will build during the series.

Check out this short video to see what you will build during the course 👇

You can safely use this code as you like, as long as you respect the terms and agreement of the MIT License.

<<< Using all the tools suggested in the course will be free of charge, except the ones from Lesson 7, where you will be deploying your application to GCP which will cost you ~20$. >>>

🤔 1. What You Will Learn

At the end of this 7 lessons course, you will know how to:

design a batch-serving architecture
use Hopsworks as a feature store
design a feature engineering pipeline that reads data from an API
build a training pipeline with hyper-parameter tunning
use W&B as an ML Platform to track your experiments, models, and metadata
implement a batch prediction pipeline
use Poetry to build your own Python packages
deploy your own private PyPi server
orchestrate everything with Airflow
use the predictions to code a web app using FastAPI and Streamlit
use Docker to containerize your code
use Great Expectations to ensure data validation and integrity
monitor the performance of the predictions over time
deploy everything to GCP
build a CI/CD pipeline using GitHub Actions

If that sounds like a lot, don't worry. After you cover this course, you will understand everything we said before. Most importantly, you will know WHY we used all these tools and how they work together as a system.

🤌 2. Lessons & Tutorials

The course consists of 7 lessons hosted on Medium Towards Data Science publication. We also provide a bonus lesson where we openly discuss potential improvements that could be made to the current architecture and trade-offs we had to take during the course. The course adds up to 2.5 hours of reading and video materials.

We recommend running the code along the articles to get the best out of this course, as we provide detailed instructions to set everything up.

👇 Access the step-by-step lessons on Medium 👇

Here is an article presenting a high-level overview of all the components you will build during the course.

📊 3. Data

We used an open API that provides hourly energy consumption values for all the energy consumer types within Denmark.

They provide an intuitive interface where you can easily query and visualize the data. You can access the data here.

The data has 4 main attributes:

Hour UTC: the UTC datetime when the data point was observed.
Price Area: Denmark is divided into two price areas: DK1 and DK2 - divided by the Great Belt. DK1 is west of the Great Belt, and DK2 is east of the Great Belt.
Consumer Type: The consumer type is the Industry Code DE35, owned and maintained by Danish Energy.
Total Consumption: Total electricity consumption in kWh

Note: The observations have a lag of 15 days! But for our demo use case, that is not a problem, as we can simulate the same steps as it would be in real time.

IMPORTANT OBSERVATION

The API will become obsolete during 2023. Its latest data points are from June 2023, and the API will become unavailable during 2023. We created a copy of the data from 2020-07-01 and 2023-06-30 to bypass this issue. Thus, there are 3 years of data to play with. More than enough for the purpose of this course. The file is stored in Google Drive accessible at this link.

Thus, instead of querying the API, we will mock the same behavior by loading the data from the file. Therefore, you don't have to download your file yourself. The code will download it and load the data from the file instead of the API, simulating 100% the same behavior.

The data points have an hourly resolution. For example: "2023–04–15 21:00Z", "2023–04–15 20:00Z", "2023–04–15 19:00Z", etc.

We will model the data as multiple time series. Each unique price area and consumer type tuple represents its unique time series.

Thus, we will build a model that independently forecasts the energy consumption for the next 24 hours for every time series.

Check out this video to better understand what the data looks like.

🧬 4. Code Structure

The code is split into two main components: the pipeline and the web app.

The pipeline consists of 3 modules:

feature-pipeline
training-pipeline
batch-prediction-pipeline

The web app consists of other 3 modules:

app-api
app-frontend
app-monitoring

Also, we have the following folders:

airflow : Airflow files | Orchestration
.github : GitHub Actions files | CI/CD
deploy : Build & Deploy

To follow the structure in its natural flow, read the folders in the following order:

feature-pipeline
training-pipeline
batch-prediction-pipeline
airflow
app-api
app-frontend & app-monitoring
.github

Read the Medium articles listed in the Lessons & Tutorials section for the whole experience.

🔧 5. Set Up Additional Tools

The code is tested only on Ubuntu 20.04 and 22.04 using Python 3.9.

We use a .env file to store all our credentials. Every module that needs a .env file has a .env.default in the module's main directory that acts as a template. Thus, you have to run:

cp .env.default .env

... and complete what is surrounded by <...>. For now, don't do anything. We will explain in detail in later steps what you have to do.

If you have problems during the setup, please leave us an issue, and we will respond to you and update the README for future readers.

Also, if you have any questions, you can contact me directly on LinkedIn.

Poetry

`<< free usage >>`

Note: During the course, we used Poetry 1.4.2. To avoid potential issues when installing the dependencies using Poetry, we recommend you use the same version (or if there are any errors & you have a different version, you can delete and regenerate the poetry.lock file).

Install Python system dependencies:

sudo apt-get install -y python3-distutils

Download and install Poetry:

curl -sSL https://install.python-poetry.org | python3 -

Open the .bashrc file to add the Poetry PATH:

nano ~/.bashrc

Add export PATH=~/.local/bin:$PATH

to ~/.bashrc

Check if Poetry is installed:

source ~/.bashrc
poetry --version

If necessary, here are the official Poetry installation instructions.

macOS M1/M2 Poetry Issues

!!! If you have issues creating Poetry environments on macOS M1/M2 devices, Hongnan Gao implemented a script that will solve all the dependency issues. Just run the following before creating a Poetry environment:

bash scripts/install_poetry_macos_m1_chip.sh

Docker

`<< free usage >>`

During the course we used Docker version 24.0.5.

Configure Credentials for the Private PyPi Server

`<< free usage >>`

We will run the private PyPi server using Docker down the line. But it will already expect the credentials configured.

Create credentials using passlib:

# Install dependencies.
sudo apt install -y apache2-utils
pip install passlib

# Create the credentials under the energy-forecasting name.
mkdir ~/.htpasswd
htpasswd -sc ~/.htpasswd/htpasswd.txt energy-forecasting

Set poetry to use the credentials:

poetry config repositories.my-pypi http://localhost
poetry config http-basic.my-pypi energy-forecasting <password>

Check that the credentials are set correctly in your poetry auth.toml file:

cat ~/.config/pypoetry/auth.toml

Hopsworks

`<< free usage >>`

You will use Hopsworks as your serverless feature store. Thus, you have to create an account and a project on Hopsworks. We will show you how to configure the code to use your Hopsworks project later.

We explained on Medium in Lesson 1 how to create a Hopsworks API Key. But long story short, you can go to your Hopsworks account settings and get the API Key from there. Afterward, you must create a new project (or use the default one) and add these credentials to the .env file under the FS_ prefix.

!!! Be careful to name your project differently than energy_consumption, as Hopsworks requires unique names across its serverless deployment.

Click here to start with Hopsworks.

Note: Our course will use only the Hopsworks freemium plan, making it free of charge to replicate the code within the series.

Weights & Biases

`<< free usage >>`

You will use Weights & Biases as your serverless ML platform. Thus, you must create an account and a project on Weights & Biases. We will show you how to configure the code to use your W&B project later.

On Medium, we explained in Lesson 2 how to create an API Key on W&B. But long story short, you can go to your W&B and create an entity & project. Afterward, you must navigate to user settings and create the API Key from there. In the end, you must add these credentials to the .env file under the WANDB_ prefix.

!!! Be careful to name your entity differently than teaching-mlops, as W&B requires unique names across its serverless deployment.

Click here to start with Weights & Biases.

Note: Our course will use only the W&B freemium plan, making it free of charge to replicate the code within the series.

GCP

First, you must install the gcloud GCP CLI on your machine.

Follow this tutorial to install it.

If you only want to run the code locally, go straight to the "Storage" section.

As before, you have to create an account and a project on GCP. Using solely the bucket as storage will be free of charge.

When we were writing this documentation, GCS was free until 5GB.

Storage

`<< free usage >>`

At this step, you have to do 5 things:

create a project
create a non-public bucket
create a service account that has admin permissions to the newly created bucket
create a service account that has read-only permissions to the newly created bucket
download a JSON key for the newly created service accounts.

Your bucket admin service account should have assigned the following role: Storage Object Admin
Your bucket read-only service account should have assigned the following role: Storage Object Viewer

NOTE: When we were writing this documentation, GCS was free until 5GB.

Check out Lesson 3 on Medium to better understand how we set up the GCP bucket and its role in the batch prediction pipeline..

NOTE: Don't forget to add the GCP credentials to the .env file under the GOOGLE_CLOUD_ prefix:

GOOGLE_CLOUD_PROJECT: your project name (e.g., "energy_consumption")
GOOGLE_CLOUD_BUCKET_NAME: your bucket name (e.g., "hourly-batch-predictions")
GOOGLE_CLOUD_SERVICE_ACCOUNT_JSON_PATH: absolute path to your JSON key file. (e.g., "/absolute/path/to/your/service-account.json")

Deployment

`<< ~20$ >>`

This step must only be finished if you want to deploy the code on GCP VMs and build the CI/CD with GitHub Actions.

Note that this step might result in a few costs on GCP. It won't be much. While developing this course, we spent only ~20$.

Also, you can get some free credits if you create a new GCP account (we created a new account and received 300$ in GCP credits). Just be sure to delete the resources after you finish the course.

See this document for detailed instructions.

🔎 6. Usage

The code is fully tested on Ubuntu 20.04 & 22.04 using Python 3.9 and Poetry 1.4.2.

Note: If you are working on macOS M1/M2, be sure to check the macOS M1/M2 Poetry Issues section.

If you have problems during the usage instructions, please leave us an issue, and we will respond to you and update the README for future readers.

Also, if you have any questions, you can contact me directly on LinkedIn.

The Pipeline

Check out Lesson 4 on Medium to better understand how everything is orchestrated using Airflow.

Run

You will run the pipeline using Airflow (free usage). Don't be scared. Docker makes everything very simple to set up.

Note: We also hooked the private PyPi server in the same docker-compose.yaml file with Airflow. Thus, everything will start with one command.

Important: If you plan to run the pipeline outside Airflow, be sure to check the 🧑‍💻 7. Installation & Usage for Development section.

Run:

# Move to the airflow directory.
cd airflow

# Make expected directories and environment variables
mkdir -p ./logs ./plugins
sudo chmod 777 ./logs ./plugins

# It will be used by Airflow to identify your user.
echo -e "AIRFLOW_UID=$(id -u)" > .env
# This shows where our project root directory is located.
echo "ML_PIPELINE_ROOT_DIR=/opt/airflow/dags" >> .env

Now from the airflow directory move to the dags directory and run:

cd ./dags

# Make a copy of the env default file.
cp .env.default .env
# Open the .env file and complete the FS_API_KEY, FS_PROJECT_NAME and WANDB_API_KEY credentials 

# Create the folder where the program expects its GCP credentials.
mkdir -p credentials/gcp/energy_consumption
# Copy the GCP service credetials that gives you admin access to GCS. 
cp -r /path/to/admin/gcs/credentials/admin-buckets.json credentials/gcp/energy_consumption
# NOTE that if you want everything to work outside the box your JSON file should be called admin-buckets.json.
# Otherwise, you have to manually configure the GOOGLE_CLOUD_SERVICE_ACCOUNT_JSON_PATH variable from the .env file.

Now go back to the airflow directory and run the following:

cd ..

# Initialize the Airflow database
docker compose up airflow-init

# Start up all services
# Note: You should set up the private PyPi server credentials before running this command.
docker compose --env-file .env up --build -d

Read the official Airflow installation using Docker, but NOTE that we modified their official docker-compose.yaml file.

Wait a while for the containers to build and run. After access 127.0.0.1:8080 to login into Airflow.
Use the following default credentials to log in:

username: airflow
password: airflow

Before starting the pipeline DAG, you must deploy the modules to the private PyPi server. Go back to the root folder of the energy-forecasting repository and run the following to build and deploy the pipeline modules to your private PyPi server:

# Set the experimental installer of Poetry to False. For us, it crashed when it was on True.
poetry config experimental.new-installer false
# Build & deploy the pipelines modules.
sh deploy/ml-pipeline.sh

Airflow will know how to install the packages from the private PyPi server.

One final step is to configure the parameters used to run the pipeline. Go to the Admin tab, then hit Variables. There you can click on the blue + button to add a new variable. These are the three parameters you can configure with our suggested values:

ml_pipeline_days_export = 30
ml_pipeline_feature_group_version = 5
ml_pipeline_should_run_hyperparameter_tuning = False

Now, go to the DAGS/All section and search for the ml_pipeline DAG. Toggle the activation button. It should automatically start in a few seconds. Also, you can manually run it by hitting the play button from the top-right side of the ml_pipeline window.

That is it. You can run the entire pipeline with a single button if all the credentials are set up correctly. How cool is that?

Here is what the DAG should look like 👇

Clean Up

docker compose down --volumes --rmi all

Backfil Using Airflow

Find your airflow-webserver docker container ID:

docker ps

Start a shell inside the airflow-webserver container and run airflow dags backfill as follows (in this example, we did a backfill between 2023/04/11 00:00:00 and 2023/04/13 23:59:59):

docker exec -it <container-id-of-airflow-webserver> sh
airflow dags backfill --start-date "2023/04/11 00:00:00" --end-date "2023/04/13 23:59:59" ml_pipeline

If you want to clear the tasks and run them again, run these commands:

docker exec -it <container-id-of-airflow-webserver> sh
airflow tasks clear --start-date "2023/04/11 00:00:00" --end-date "2023/04/13 23:59:59" ml_pipeline

Run Private PyPi Server Separately

The private PyPi server is already hooked to the airflow docker compose file. But if you want to run it separately for whatever reason, you can run this command instead:

docker run -p 80:8080 -v ~/.htpasswd:/data/.htpasswd pypiserver/pypiserver:v1.5.2 run -P .htpasswd/htpasswd.txt --overwrite

The Web App

Check out Lesson 6 on Medium to better understand how the web app components work together.

Fortunately, everything is a lot simpler when setting up the web app. This time, we need to configure only a few credentials.

Important: If you plan to run the web app components without docker-compose, check the 🧑‍💻 7. Installation & Usage for Development section.

Copy the bucket read-only GCP credentials to the root directory of your energy-forecasting project:

# Create the folder where the program expects its GCP credentials.
mkdir -p credentials/gcp/energy_consumption
# Copy the GCP service credetials that gives you read-only access to GCS. 
cp -r /path/to/admin/gcs/credentials/read-buckets.json credentials/gcp/energy_consumption
# NOTE that if you want everything to work outside the box your JSON file should be called read-buckets.json.
# Otherwise, you have to manually configure the APP_API_GCP_SERVICE_ACCOUNT_JSON_PATH variable from the .env file of the API.

Go to the API folder and make a copy of the .env.default file:

cd ./app-api
cp .env.default .env

NOTE: Remember to complete the .env file with your own variables.

That is it!

Go back to the root directory of your energy-forecasting project and run the following docker command, which will build and run all the docker containers of the web app:

docker compose -f deploy/app-docker-compose.yml --project-directory . up --build

If you want to run it in development mode, run the following command:

docker compose -f deploy/app-docker-compose.yml -f deploy/app-docker-compose.local.yml --project-directory . up --build

Now you can see the apps running at:

Deploy the Code to GCP

Check out this section.

Set UP CI/CD with GitHub Actions

Check out this section.

🧑‍💻 7. Installation & Usage for Development

All the modules support Poetry. Thus the installation is straightforward.

Note 1: Just ensure you have installed Python 3.9, not Python 3.8 or Python 3.10.

Note 2: During the course, we used Poetry 1.4.2. To avoid potential issues when installing the dependencies using Poetry, we recommend you use the same version (or if there are any errors & you have a different version, you can delete and regenerate the poetry.lock file).

Note 3: If you are working on macOS M1/M2, be sure to check the macOS M1/M2 Poetry Issues section.

The Pipeline

We support Docker to run the whole pipeline. Check out the Usage section if you only want to run it as a whole.

If Poetry is not using Python 3.9, you can follow the next steps:

Install Python 3.9 on your machine.
cd /path/to/project, for example, cd ./feature-pipeline
run which python3.9 to find where Python3.9 is located
run poetry env use /path/to/python3.9

Every pipeline component must load its credential from the .env file. Thus, you have two options:

Recommended option: run cp .env.default .env into the folder where the ML_PIPELINE_ROOT_DIR env var is pointing to & fill in the credentials of the .env file. Check the section below to see how to set it up.
Create a copy by running cp .env.default .env in every pipeline directory individually. But note that by taking this approach, you won't be able to run the system as a whole.

See here how to install every project individually:

Set Up the ML_PIPELINE_ROOT_DIR Variable

Important: Before installing and running every module individually, one key step is to set the ML_PIPELINE_ROOT_DIR variable to your root directory of the energy-forecasting project (or any other directory - just make sure to set it): Export it to your ~/.bashrc file:

gedit ~/.bashrc
export ML_PIPELINE_ROOT_DIR=/path/to/root/directory/repository/energy-forecasting/

Or run every Python script proceeded by the ML_PIPELINE_ROOT_DIR variables. For example:

ML_PIPELINE_ROOT_DIR=/path/to/root/directory/repository/energy-forecasting/ python -m feature_pipeline.pipeline

By doing so, all the 3 pipeline projects (feature, training, batch) will load and save the following files from the same location:

.env configuration;
JSON metadata files;
logs & plots.

NOTE: This step is critical as every pipeline component needs to access the JSON metadata from other pipeline processes. By setting up the ML_PIPELINE_ROOT_DIR variable, all the metadata JSON files will be saved and accessed from the same location between different processes. For example, the batch prediction pipeline will read the model version it needs to use to make predictions from a JSON file generated by the training pipeline. Without settings the ML_PIPELINE_ROOT_DIR, the training and batch processes won't share the same output directory. Thus, they won't know how to talk to each other. When running the project inside Airflow, it is defaulted to /opt/airflow/dags; thus, you must set this variable only when running it outside Airflow.

The Web App

We support Docker to run the web app. Check out the Usage section if you only want to run it as a whole.

See here how to install every project individually:

You can also run the whole web app in development mode using Docker:

docker compose -f deploy/app-docker-compose.yml -f deploy/app-docker-compose.local.yml --project-directory . up --build

🏆 8. Licensing & Contributing

The code is under the MIT License. Thus, as long as you keep distributing the License, feel free to share, clone, or change the code as you like.

Also, if you find any bugs or missing pieces in the documentation, I encourage you to add an issue on GitHub or a PR. Based on your support, I will adapt the code and docs for future readers.

Furthermore, you can contact me directly on LinkedIn if you have any questions.

I also want to thank Kurtis Pykes for being an awesome copilot and helping me make this course happen.

Let's connect if you want to level up in designing and productionizing ML systems:

I post almost daily AI content on 👇🏼

Subscribe to my ML engineering weekly newsletter.

🎨 Creating content takes me a lot of time. If you enjoyed my work, you could support me by:

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Thank you ✌🏼 !

Name		Name	Last commit message	Last commit date
Latest commit History 383 Commits
.github/workflows		.github/workflows
airflow		airflow
app-api		app-api
app-frontend		app-frontend
app-monitoring		app-monitoring
batch-prediction-pipeline		batch-prediction-pipeline
deploy		deploy
feature-pipeline		feature-pipeline
images		images
scripts		scripts
training-pipeline		training-pipeline
.env.default		.env.default
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
README_CICD.md		README_CICD.md
README_DEPLOY.md		README_DEPLOY.md

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The Full Stack 7-Steps MLOps Framework

Table of Contents

🤔 1. What You Will Learn

🤌 2. Lessons & Tutorials

📊 3. Data

IMPORTANT OBSERVATION

🧬 4. Code Structure

🔧 5. Set Up Additional Tools

Poetry

<< free usage >>

macOS M1/M2 Poetry Issues

Docker

<< free usage >>

Configure Credentials for the Private PyPi Server

<< free usage >>

Hopsworks

<< free usage >>

Weights & Biases

<< free usage >>

GCP

Storage

<< free usage >>

Deployment

<< ~20$ >>

🔎 6. Usage

The Pipeline

Run

Clean Up

Backfil Using Airflow

Run Private PyPi Server Separately

The Web App

Deploy the Code to GCP

Set UP CI/CD with GitHub Actions

🧑‍💻 7. Installation & Usage for Development

The Pipeline

Set Up the ML_PIPELINE_ROOT_DIR Variable

The Web App

🏆 8. Licensing & Contributing

Let's connect if you want to level up in designing and productionizing ML systems:

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