Serving Machine Learning Model with MLFlow

Serving Machine Learning Model with MLFlow

We are going to briefly follow the Quickstart in the official MLFlow Documentation. We will create, serve and invoke a Machine Learning Model with MLFlow.


pip3 install mlflow

Create a new folder for our little project and create a new file called

import pandas as pd
import mlflow.pyfunc

class Churn_one(mlflow.pyfunc.PythonModel):
    def __init__(self, n):
        self.n = n

    def predict(self, context, model_input):
        return model_input.apply(lambda column: column + self.n)

# Construct and save the model
model_path = "Churn_one"
Churn_one = Churn_one(n=5)
mlflow.pyfunc.save_model(path=model_path, python_model=Churn_one)

# Load the model in `python_function` format
loaded_model = mlflow.pyfunc.load_model(model_path)

model_input = pd.DataFrame([range(10)])
model_output = loaded_model.predict(model_input)
assert model_output.equals(pd.DataFrame([range(5, 15)]))

Briefly explained: we import mlflow. We create a Class that which is kind of our Class Model/Classifier. With the next three lines we save our Class as a „pyfunc“ model. You can read what a pyfunc is here. Now execute this file with


MLFlow will create a new Folder „Churn_one“. This Folder is a self-contained Version of the Class-Model from the previous file. Inside this Folder we see following:

New Folder with new Files inside the MLFLOW Model.

The conda.yaml is the definition of your python environment that is needed to make your Model work. MLmodel is a File that contains meta-data about your model – the syntax is very poorly explained in the official docs. The pkl will contain pickled data for our model.

Serving the Model with MLFlow

Now we need to serve our Machine Learning Model. You can either create a Flask server, like we discussed in this post. But we are going to use the built-in feature of MLFlow serve. This will spawn a Flask Server for us and do all the work. We start a ML Server with

mlflow models serve -m Churn_one -h -p 5000
Serving on

Now we can invoke prediction process for our model. We have to create a POST Request to http://localhost:5000/invocations . Obviously, if you deploy this server into an EC2 Instance or another public server, you’ll be able to call your public ip to invoke your ML Model like: http:PUBLIC_IP:5000/invocations.

Check out the Mlflow Online Courses

I’ll use a UI Request Generator and create a POST Request. I expect the ML Server to receive this Request, make some predictions with the model, and give me back some results. The data that our Model needs to run predictions, will be supplied in the POST Request in JSON Format.

POST Request to MLFlow Server with Results returned.

In the Screenshot above you see the POST Request to our Server and the JSON Payload with our Prediction Data. The data has to be in JSON Format. And this Format has to have keys of Columns and Data. These Columns will be converted to a pandas DataFrame. This DataFrame will then be used to insert into your model to run the predictions.

Below is the result of running predictions on our Model with the supplied data. And that is it. The Workflow is pretty simple once you got it. You create an MLModel out of your current Machine Learning Model. You use this new MLModel Format to serve your model. The hardest part is most probably the correct converting of your Python Model Classes/Function into the MLModel Format without any limits.

MLFlow vs. Metaflow

MLFlow vs. Metaflow

In short: MLFlow is far superior than Metaflow. So Learn MLFlow now.

We compare two Machine Learning and Data Science frameworks – MLFlow vs. Metaflow. These Data Science and Machine Learning Frameworks are the most popular in their category – ML. They provide you a fixed set or best practices, methods, classed and helping tools (like UIs or APIs). They support your ML or DS Project Lifecycle.

If you want to learn more about Machine Learning, consider browsing through our Online Course Section!

Introduction MLFlow

MLFlow was developer and open sourced by Databricks. Here is how MetaFlow describes itself in their Intro Blog Post:

MLflow is designed to work with any ML library, algorithm, deployment tool or language. It’s built around REST APIs and simple data formats (e.g., a model can be viewed as a lambda function) that can be used from a variety of tools, instead of only providing a small set of built-in functionality. This also makes it easy to add MLflow to your existing ML code so you can benefit from it immediately, and to share code using any ML library that others in your organization can run.

Introduction Metaflow

The official Metaflow description of itself is very good, so here goes copy & paste:

Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

Our framework provides a unified API to the infrastructure stack. It’s required to execute data science projects – from prototype to production.

Community, Popularity and Reliability

The popularity of framework is pretty important. Here is why. When you run into a problem, you will turn to Google and StackOverflow for help and you will beg that someone knows what framework you are using. So here is a Google Trends for MLFlow vs Metaflow. As you can see below, metaflow peaked shortly in late 2019. But overall, MLFlow wins.

MLFlow vs. MetaFlow

Another critical point is the Community behind a Codebase. The bigger the community the more reliable (in most cases) the codebase. The following Github Screenshots speak for themselves. And MLFLow wins again.


MLFlow has more than 160 Contributors and is forked around 1200 times.


Metaflow has more than 15 Contributors and is forked around 190 times.

Meta-Logging, Training, Deploying

A ML Framework should provide us with a healthy balance between concrete ways to implement a new project – which reduces complexity. But it also should give us enough freedom to experiment and stay flexible for unusual business/technical environments.

The most pressing issue, in my experience, is the fact that when you train your model locally, you don’t save any data about this process. Re-producing the same results may be difficult. Or even a more simple task, just retrieve the training results from last week…you can’t, cause you started to tune your hyperparameters and retrained your model 10 times more that day. An ML framework should collect metadata about these and other similar processes.

MLFlow Tutorial

Let’s take a look at MLFlow from a practical side. Installation, Usage, UI, etc.

pip3 install mlflow

Usage: mlflow [OPTIONS] COMMAND [ARGS]...

  --version  Show the version and exit.
  --help     Show this message and exit.

  artifacts    Upload, list, and download artifacts from an MLflow artifact...
  azureml      Serve models on Azure ML.
  db           Commands for managing an MLflow tracking database.
  experiments  Manage experiments.
  models       Deploy MLflow models locally.
  run          Run an MLflow project from the given URI.
  runs         Manage runs.
  sagemaker    Serve models on SageMaker.
  server       Run the MLflow tracking server.
  ui           Launch the MLflow tracking UI for local viewing of run...

mlflow ui
Serving on http://localhost:5000

Now let’s run the basic dummy script below to generate some meta/tracking data.

import mlflow
# Start an MLflow run

with mlflow.start_run():
  # Log a parameter (key-value pair)
  mlflow.log_param("param2", 3)

  # Log a metric; metrics can be updated throughout the run
  mlflow.log_metric("foo", 2, step=1)
  mlflow.log_metric("foo", 4, step=2)
  mlflow.log_metric("foo", 6, step=3)

  # Log an artifact (output file)
  with open("output.txt", "w") as f:
      f.write("Hello world!")

MLFlow UI Experiments

On the frontpage you can see all the executions that i executed either with „mlflow run …“ or with „python“.

You can see even those executions that failed. Which is pretty awesome for developing and debugging.

Extremely important is the feature of comparing different experiments with one another which you can do on this page.

MLFlow UI Params

On the Single Experiment Page you’ll see stuff like Date, User, Source, Duration of this particular Experiment (Execution, Flow).

You also can see Tags and a List of Artifacts with Preview mode – which is awesome. In our case the dummy code from above generated a text file with „Hello World“ in it!

MLFlow UI Metric Chart

Another awesome feature of MLFLow is the chart which will display your metrics (that you set up manually) in a chart.

MetaFlow Tutorial

First of all, i’m on Windows. You can’t use MetaFlow on Windows without some crazy tweaks. In fact, MetaFlow said they won’t support Windows. So this is already is a nogo for me (shouldn’t be a problem if you are on Linux). For the purpose of demonstration i’ll boot a Linux EC2 Instance and play around there – obviously, you can’t develop effectively with EC2.

$ pip3 install metaflow==2.0.1

$ metaflow
Metaflow (2.0.1): More data science, less engineering - Read the documentation - Chat with us        - Get help by email

  metaflow tutorials  Browse and access metaflow tutorials.
  metaflow configure  Configure metaflow to access the cloud.
  metaflow status     Display the current working tree.
  metaflow help       Show all available commands to run.

$ metaflow tutorials pull
Metaflow (2.0.1)

Pulling episode "00-helloworld" into your current working directory.
Pulling episode "01-playlist" into your current working directory.
Pulling episode "02-statistics" into your current working directory.
Pulling episode "03-playlist-redux" into your current working directory.
Pulling episode "04-playlist-plus" into your current working directory.
Pulling episode "05-helloaws" into your current working directory.
Pulling episode "06-statistics-redux" into your current working directory.
Pulling episode "07-worldview" into your current working directory.

To know more about an episode, type:
metaflow tutorials info [EPISODE]

$ cd metaflow-tutorials/00-helloworld

That’s it. MetaFlow has a fancy CLI and no UI (to my knowledge). Compared to MLFlow, Metaflow has a more granular process control. For example, you can track and manage every single method in your Python code. They are so called steps in MetaFlow and they (python methods – metaflow steps) are controlled via Python decorators. The file „“ file contains a so called „Flow“ (Collection of „steps“ or python methods).

$ python3 show
Metaflow 2.0.1 executing HelloFlow for user:ubuntu

A flow where Metaflow prints 'Hi'.

Run this flow to validate that Metaflow is installed correctly.

Step start
    This is the 'start' step. All flows must have a step named 'start' that
    is the first step in the flow.
    => hello

Step hello
    A step for metaflow to introduce itself.
    => end

Step end
    This is the 'end' step. All flows must have an 'end' step, which is the
    last step in the flow.

$ python3 run
Metaflow 2.0.1 executing HelloFlow for user:ubuntu
Validating your flow...
    The graph looks good!
Running pylint...
    Pylint not found, so extra checks are disabled.
Creating local datastore in current directory (/home/ubuntu/metaflow-tutorials/00-helloworld/.metaflow)
2020-01-30 14:50:52.288 Workflow starting (run-id 1580395852284119):
2020-01-30 14:50:52.291 [1580395852284119/start/1 (pid 18456)] Task is starting.
2020-01-30 14:50:52.660 [1580395852284119/start/1 (pid 18456)] HelloFlow is starting.
2020-01-30 14:50:52.691 [1580395852284119/start/1 (pid 18456)] Task finished successfully.
2020-01-30 14:50:52.695 [1580395852284119/hello/2 (pid 18462)] Task is starting.
2020-01-30 14:50:53.068 [1580395852284119/hello/2 (pid 18462)] Metaflow says: Hi!
2020-01-30 14:50:53.101 [1580395852284119/hello/2 (pid 18462)] Task finished successfully.
2020-01-30 14:50:53.105 [1580395852284119/end/3 (pid 18468)] Task is starting.
2020-01-30 14:50:53.478 [1580395852284119/end/3 (pid 18468)] HelloFlow is all done.
2020-01-30 14:50:53.511 [1580395852284119/end/3 (pid 18468)] Task finished successfully.
2020-01-30 14:50:53.512 Done!

You see a pretty verbose logging over what step (method) is being executed. Every single Step like „start“, „hello“ and „end“ corresponds to a python method inside the „“ file.

Conclusion – MLFlow vs. Metaflow

Short version: go with MLFlow!

And here is why: Metaflow lacks an overview or a UI that will make this metadata, logging & tracking more accessible to us developers. Also the easy comparison between flows or models isn’t there. Metaflow seems to be highly intertwined with AWS (Sagemaker), which is great.

Compared to a framework that integrates also with Google Cloud, MS Azure, etc. – not so great. The concept of steps gives you a granular control over your Project. Also, you can define python dependencies on method level – meaning that every step could have it’s own versions of libraries – which is awesome.

The Framework MLFlow tops with the less intrusive structure – it doesn’t try gain control over your methods with decorators. It has a useful UI and integrations to many Cloud providers.