{"id":1298,"date":"2025-01-20T07:03:00","date_gmt":"2025-01-20T07:03:00","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/20\/how-to-log-your-data-with-mlflow-23c8027e4021\/"},"modified":"2025-01-20T07:03:00","modified_gmt":"2025-01-20T07:03:00","slug":"how-to-log-your-data-with-mlflow-23c8027e4021","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/20\/how-to-log-your-data-with-mlflow-23c8027e4021\/","title":{"rendered":"How to Log Your Data with MLflow"},"content":{"rendered":"

How to Log Your Data with MLflow
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MLflow, MLOps, Data\u00a0Science<\/h4>\n

Mastering data logging in MLOps for your AI\u00a0workflow<\/h4>\n
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Photo by Chris Liverani<\/a> on\u00a0Unsplash<\/a><\/figcaption><\/figure>\n

Preface<\/h3>\n

Data is one of the most critical components of the machine learning process.<\/strong> In fact, the quality of the data used in training a model often determines the success or failure of the entire project. While algorithms and models are important, they are powerless without data that is accurate, clean, and representative of the problem you\u2019re trying to solve. Whether you are dealing with structured data, unstructured data, or large-scale datasets, the preparation and understanding of data lay the foundation for any machine learning system. A well-curated dataset can provide the necessary signals for a model to learn effectively, while poor or biased data can lead to incorrect predictions, overfitting, or even harmful societal impacts when models are deployed in real-world applications.<\/p>\n

With the increasing complexity of machine learning workflows, ensuring the reproducibility and traceability of experiments has become a key concern. This is where MLOps<\/strong> (Machine Learning Operations) comes into play. MLOps is the practice of bringing together data science and operations to automate and streamline machine learning workflows. One critical aspect of MLOps is the tracking of datasets throughout the entire lifecycle of a machine learning project. <\/strong>Tracking is not just about capturing the parameters and metrics of models; it\u2019s equally important to log the datasets used during each phase of the process. This ensures that, down the line, when models are re-evaluated or retrained, the same datasets can be referenced, tested, or reused. It allows for better comparison of results, understanding of changes, and most importantly, ensuring that the results are reproducible by others. As a best practice for modern ML systems, I recommend that all ML practitioners worldwide log their data during training.<\/strong><\/p>\n

Data Yesterday vs. Data\u00a0Today?<\/p><\/blockquote>\n

In this context, MLflow<\/strong> plays a pivotal role by offering a suite of tools that help streamline these MLOps practices. One of the key components of MLflow is its <\/strong>mlflow.data module, which provides the ability to log datasets as part of machine learning experiments.<\/strong> The mlflow.data module ensures that datasets are properly documented, their metadata is tracked, and they can be retrieved and reused in future runs. This helps prevent common problems like “data drift” where models start to perform worse because of subtle changes in the underlying data. MLflow\u2019s ability to track datasets alongside models and parameters ensures that any new experiment can reliably compare results against previous runs using the exact same data, providing transparency and\u00a0clarity.<\/p>\n

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Photo by Brian McGowan<\/a> on\u00a0Unsplash<\/a><\/figcaption><\/figure>\n

Overview<\/h3>\n

This article will guide you through the best practices of logging a dataset in MLflow using the California Housing Dataset as an example.<\/strong> We\u2019ll explore mlflow.data interfaces and demonstrate how you can track and manage your dataset during an ML experiment.<\/p>\n

Imagine you are a data scientist working on a project to predict housing prices in California based on various features such as median income, population, and location. You\u2019ve spent hours curating a dataset from multiple sources, cleaning it, and ensuring it\u2019s ready for training. Now you\u2019re ready to run your machine learning experiments. Logging your dataset at this stage is crucial because it serves as a snapshot of the exact data used in this specific training run. Should you need to revisit the experiment months later\u200a\u2014\u200aperhaps to improve the model, tune hyperparameters, or audit the results\u200a\u2014\u200ayou want to ensure that you are using the same dataset to maintain consistency and comparability. Without logging your dataset, it\u2019s easy to lose track of which version of the data was used, especially if the data is updated or changed over\u00a0time.<\/p>\n

Without further ado, let\u2019s get\u00a0started!<\/p>\n

Set Up<\/h3>\n

Setting up an MLflow server locally is straightforward. Use the following command:<\/p>\n

mlflow server --host 127.0.0.1 --port 8080<\/pre>\n
Then set the tracking\u00a0URI.<\/p>\n
mlflow.set_tracking_uri(\"http:\/\/127.0.0.1:8080\")<\/pre>\n
For more advanced configurations, refer to the MLflow documentation<\/a>.<\/p>\n
The California housing\u00a0dataset<\/h3>\n
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Photo by Robert Bye<\/a> on\u00a0Unsplash<\/a><\/figcaption><\/figure>\n
For this article, we are using the California housing dataset (CC BY license). However, you can apply the same principles to log and track any dataset of your\u00a0choice.<\/p>\n
For more information on the California housing dataset, refer to this\u00a0doc<\/a>.<\/p>\n
Dataset and DatasetSource<\/h3>\n
mlflow.data.dataset.Dataset<\/h4>\n
Before diving into dataset logging, evaluation, and retrieval, it\u2019s important to understand the concept of datasets in MLflow. MLflow provides the mlflow.data.dataset.Dataset object, which represents datasets used in with MLflow Tracking.<\/p>\n
class mlflow.data.dataset.Dataset(source: mlflow.data.dataset_source.DatasetSource, name: Optional[str] = None, digest: Optional[str] = None)<\/pre>\n
This object comes with key properties:<\/p>\n