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Are Your ML Experiments a Mess? Here’s the Fix

A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. Alex Davis July 21, 20267 min read Photo by Stephen Dawson via Unsplash Imagine you are building a new model. You get decent baseline results with a simple model, but try to improve performance with more advanced models. During development, your PM acquires some additional data that could prove useful, and can now revert to a simpler model. After extensive hyperparameter tuning, you consult your team and they advise you to optimize for a different performance metric. After you finally deliver a model, your stakeholder changes the direction of the project, requiring a complete overhaul. This is a common situation for data scientists, especially those with cross-collaborative teams, stakeholders, and changing priorities. Model development can quickly become a wild goose chase of figuring out requirements, data sources, performance metrics, etc. Through all of this chaos, it is critical that teams are able to not only keep track of all phases of model development, but are also able to reproduce results quickly. You need to model governance.

Model Governance

Model governance refers to the framework by which a team maintains controls around its use of models, including experiment tracking, version control, reproducibility, etc. It is a critical capability that many teams do not consider until their models are already in production. It is important to have a process to effectively track development efforts, models in production, and data leveraged so that organizations can ensure quality, compliance, and monitor machine learning integration. Learn this step by step with the interactive Machine Learning roadmap. Image by Author To ensure that the right models are in production, we need a way to manage various models and versions, track performance metrics, and reproduce results. This is where ML Flow comes in. ML Flow is an open-source ML Ops platform that allows flexible model development, deployment, and management by streamlining logging and tracking of models, metrics, and more. This article will give an introduction into how to install ML Flow, create experiments, log models and more. ···

ML Flow Tutorial

Model Development

For our model, we will construct a basic linear regression model. This is a model to predict the annual salary of data professionals using inputs that include job title, company size, etc. See data here:https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries (CC0: Public Domain). I slightly modified the data to reduce the number of options for certain features. Please note that this is a very poor model. We are using it only to demonstrate ML Flow. python

#define independent and dependent features
X = salary_data.drop(columns = 'salary_in_usd')
y = salary_data['salary_in_usd']

#split between training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
  X, y, random_state = 104, test_size = 0.2, shuffle = True)

#define parameters separately to log
params = {"fit_intercept": True,
         "positive": False}

#fit linear regression model
regr = linear_model.LinearRegression(**params)
regr.fit(X_train, y_train)

#make predictions
y_pred = regr.predict(X_test)

Note that we define the model parameters separately. This will be important when we incorporate ML Flow.

Logging Model with ML Flow

To start using ML Flow, you can navigate to the terminal and pip install it as a module. Next, run the server command to host a local server on your machine and leverage the ML Flow user interface. powershell

pip install mlflow

powershell

mlflow server --host 127.0.0.1 --port 8080

Now that we have ML Flow installed and our local server running, we can log our model. Before logging a model, we need to start a new ‘experiment’. Think of this as a project, a bin where we will log and store all related models, metrics, data. etc. In the code below, we start a new experiment titled “Logging Example”. An experiment consists of runs, which contain the model used, its parameters, metrics, artifacts, and anything else that you choose to log. As you log more runs, we can track our progress and compare the performance of the various models developed. Using the code below, we:

  1. Start the experiment
  2. Start a run titled “salary_baseline_regression”
  3. Log the adjusted R2 as a metric
  4. Log the model’s parameters
  5. Set a tag for the model
  6. Log the actual model

python

import mlflow
from mlflow.models import infer_signature

#set tracking uri
mlflow.set_tracking_uri(uri = "http://127.0.0.1:8080")

#create a new MLflow Experiment
mlflow.set_experiment("Logging Example")

#start an MLflow run
with mlflow.start_run(run_name = "salary_baseline_regression") as run:

    # Log the loss metric
    mlflow.log_metric("Adjusted R2", r2_score(y_test, y_pred))
    
    #log the hyperparameters
    mlflow.log_params(params)

    #set a tag that we can use to remind ourselves what this run was for
    mlflow.set_tag("Model Type", "Baseline")

    #log the model
    model_info = mlflow.sklearn.log_model(
        sk_model = regr,
        artifact_path = 'model',
        signature = infer_signature(X_train, regr.predict(X_train)),
        input_example = X_train,
        registered_model_name = "salary_baseline_regression")

After running, you can navigate to the ML Flow UI using the following link:http://localhost:8080/

Navigating ML Flow UI

The ML Flow UI will open on the ‘Experiments’ tab. Here, we can see the experiment we created, the run for our baseline model, the model used, as well as metrics and parameters we logged. Note that the metrics and parameters may not be displayed at first. You can add them to the table using the ‘Columns’ drop down. Image by Author Next, click on the run name to navigate to the run view. This brings us to the overview tab where we can clearly see everything we have logged (model, R2 metric, model parameters). Image by Author Next, click on the model name or navigate to the model view. Here we can view our model, add descriptions and/or tags, and keep track of it’s versions. The version number will increase every time we create a run using this model. The tags and descriptions are helpful for overall documentation and to tag models in development vs. under review vs. in production. Image by Author So far, we’ve seen how to create experiments and runs, as well as log models, metrics, parameters, etc. These are the very basics of ML Flow. Now let’s explore more options to automate some of the process and make best use out of ML Flow.

Advanced Logging Options

There are likely many metrics we would like to track, which will vary depending on the model and approach. Adding and removing metrics every time we change our approach can be laborious. However, we can feed ML Flow our test data and results to automate the logging of metrics and streamline our process. First, let’s save a copy of our test data and the results of our model. python

#merge salaries and predictions for mlfow
eval_data = X_test.copy()
eval_data["salary"] = y_test

Next, we give ML Flow our data and tell it what our target variable is (salary). Now we can add a few things to our ML Flow process:

  1. Log our data to keep track of model inputs
  2. Use mlflow.evaluate() to automate logging the model metrics

We can also log ‘artifacts’, which can be many things. During development and evaluation, it may be helpful to develop visualizations that reflect model performance. We can log those as well using mlflow.log_artifact(). python

#set tracking uri
mlflow.set_tracking_uri(uri = "http://127.0.0.1:8080")

#create an instance of a PandasDataset
dataset = mlflow.data.from_pandas(
    eval_data, source = 'kaggle', name = "ds_salaries", targets = "salary")

#create a new MLflow Experiment
mlflow.set_experiment("Salary Prediction")

#start an MLflow run
with mlflow.start_run(run_name = "salary_baseline_regression") as run:

    #log the dataset
    mlflow.log_input(dataset, context = "training")
    
    #log the hyperparameters
    mlflow.log_params(params)

    #log pngs
    mlflow.log_artifact('Result.png')

    #set a tag that we can use to remind ourselves what this run was for
    mlflow.set_tag("Model Type", "Baseline")

    #log the model
    model_info = mlflow.sklearn.log_model(
        sk_model = regr,
        artifact_path = 'model',
        signature = infer_signature(X_train, regr.predict(X_train)),
        input_example = X_train,
        registered_model_name = "salary_baseline_regression")
    model_uri = mlflow.get_artifact_uri("model")

    #create the model uri and evalute the model
    result = mlflow.evaluate(model = model_uri, data = dataset, model_type = "regressor", evaluators=["default"])

Now, if we navigate back to the ML Flow UI and click on the run, we can see that there are several metrics we are tracking. Image by Author And if we want to evaluate multiple models in a run, we can compare their performance side-by-side using the automated visualizations below. Image by Author To view the visualizations you logged, navigate to the artifacts view. Image by Author Lastly, we can now view our our data inputs and their types. It is best to log datasets as we drop or add new features, sources, etc. Image by Author

Recalling ML Flow Models

To reproduce a model that we have logged, we can easily recall models from ML Flow using the model name and version. This can also be used to retrain models that are currently in production. python

model_name = "salary_baseline_regression"
model_version = "latest"

# Load the model from the Model Registry
model_uri = f"models:/{model_name}/{model_version}"
model = mlflow.sklearn.load_model(model_uri)

Databricks Integration

If you are (like me) a fan of Databricks, ML Flow is fully integrated within the platform. No need to run ML Flow locally, as Databricks integrates the ML Flow UI within their 'Experiments' view. It also includes additional features like tracking for generative AI applications, agents, and integrates AutoML for model optimization. See full documentation here: https://docs.databricks.com/aws/en/mlflow/ ···

Conclusion

In conclusion, model governance is a must-have capability for any organization to ensure quality and compliance. Tracking and monitoring tools like ML Flow offer solutions to streamline model governance processes and fully manage model development and deployment. ··· I hope you have enjoyed my article! Please feel free to comment, ask questions, or request other topics. Connect with me on LinkedIn:https://www.linkedin.com/in/alexdavis2020/

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