Why Is Data About Data Critical In Machine Learning Projects?

Machine Learning Metadata Store: What It Is, Why It Matters, and How to Implement It

Machine learning is mainly data-driven, which involves large amounts of raw and intermediate datasets. Metadata in simple terms can be called data about data. ML metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows.

Machine learning involves big data and has the goal to create and deploy a model in production to be used by others for the greater good. To recognize the model, it is necessary to retrieve and analyse the output of the ML model at various stages and the datasets used for its creation. Data about these data are Metadata. Every run of a production ML pipeline generates metadata holding information about the various pipeline components, their executions, and resulting artifacts. Storing the metadata would succour in retraining the identical model and getting the same results. With so much experimental data flowing through the pipeline, it's necessary to segregate the metadata of each experimental model from input data. Therefore, the requirement arises for a metadata store, i.e., a database with metadata.

Types of Metadata

Data-Data used for model training and evaluation play a dominant role in comparability and reproducibility.

Model- During the training process, you need to keep track of several model attributes.

Feature pre-processing steps- TData is rarely easily available in a manner that can be used for training. But this raw data is not always fed to the model for training. In some cases, the important information which is needed for the model, i.e., the features, is picked from the raw data and becomes the model's input. Now, since we aim for reproducibility, we have to guarantee consistency in the process of the selected feature, and therefore, the feature pre-processing steps need to be saved.

Model type- To refurbish the data-driven model, store the type of model used like AlexNet, YoloV4, Random Forest, SVM, etc., with their versions and frameworks like PyTorch, Tensorflow, and Scikit-learn. It makes sure there is no ambiguity in the selection of the model when reproducing it.

Hyperparameters- The ML data-driven model generally has a loss or cost function. To generate a robust and efficient model, the aim is to minimize the loss function. The weights and biases of the model where the loss function is minimized are the hyperparameters, that need to be kept to reproduce the systematic model created earlier. This minimizes processing time in finding the right hyperparameters to tune the model and speeds up the model selection process.

Metrics- The results from the model evaluation are important in understanding how well you have built your model. They help in figuring out whether the model is overfitting to the training set or performing a thorough error analysis.

Context- Model context is information about the environment of an ML experiment that may or may not affect the experiment's output but could be a factor of change in it. It includes source code, programming language, their versions, and host information like environment variables, system packages, etc.

What is a Machine Learning Metadata store?

A "store" for machine learning model-related metadata is considered the ML metadata store. This is a one-stop shop for everything you need to know about building and deploying machine learning models.

Final Thoughts

We dare not deny the importance of data in the field of machine learning. A metadata store that has all the essential data about the data is undeniably important. According to different business needs, the right metadata store could vary from organization to organization. We have gained an overview of why metadata is required to store and the different sorts of metadata to store in this blog.