Companies have pushed machine learning further with every development no matter how small. From developing new algorithms, tools, and frameworks to creating new roles in the industry, and most importantly creating entire products that serve as data instruments to help simplify and leverage machine learning at its full force. 

Considering that machine learning has been employed in several industries, adding up products and models that automate manual, and time-consuming processes, keeping an accurate track of the newly created data is crucial. And this is the purpose of OpenMetadata, an open-source project that aids in gathering and working with metadata from one place. 

For data scientists to build efficient ML models, analyzing performance and performing predictions is not enough. It’s necessary to understand dependencies and the nature of data evolution. Therefore, Machine Learning can become more transparent, and contextual and open more ways for collaboration. 

Machine Learning and Metadata for the Moment

AI has played a major role in the development of tools, frameworks, and techniques that assist Data Scientists on a daily basis. PyCaret and MLflow are two examples of libraries that accelerate data discovery and modeling. These tools do a great job when it comes to discovering, training, deploying, and measuring models, but there are context and transparency issues that remain in the shadows. 

Each of these processes produces a significant amount of metadata such as: 

• References to model weight files 
• Parameters about model training 
• Evaluation metrics 
• Examples of predictions 
• Outputs of pipeline testings 
• Dataset versions

Moving data from one place to the other has been facilitated immensely but how much we know about the data we’re moving receives little attention. Paying more attention to the context of data, making it more transparent, and focusing on cooperation, are characteristics that turn ML solutions into actual products. Tools like OpenMetadata help you to answer crucial questions about data such as: 

• How well do we know this data? 
• Is this data clean? 
• How many times is the data refreshed? 
• Who maintains this data? 
• What exactly does each column mean?

OpenMetadata and Machine Learning

The first step to establishing a strong and reliable foundation for OpenMetadata use stands in creating a clear and Open Standard for communication to allow the fostering of collaboration. So, it makes sense to set an understandable terminology for protocols, processes, and parts of the data ecosystem. These include tables, ML models, pipelines, dashboards, and how they relate to one another. 

This standardization of metadata vocabulary allows a clear exchange of information, management of the processes and dependencies, and all the assets connected to ML models. The next step after establishing this vocabulary is connectivity and integration with other data platforms. OpenMetadata allows data ingestions through its series of connectors that support connection with messaging, pipeline, database, and dashboard services. These connectors include: 

• Airflow
• Kafka 
• Oracle 
• Databricks 
• Delta Lake 
• Redshift 
• BigQuery
• Snowflake
• Salesforce 
• Postgres 

OpenMetadata offers ML Model Entity definition which helps data scientists add more detailed metadata into Features. Such functionalities intend to make collaboration among team members easier. Since while developing ML solutions companies are faced with repetitive work, and data misuse, sharing access to this data allows everyone to contribute to its transformation and management. 

Functionalities such as lineage and tags make discovery seamless, and allow members to filter the information they seek. Data lineage provides data scientists and other teams the ability to keep track of the evolution of data over time. Having clear records of changes that happened to the metadata means less debugging. 

Final Thoughts 

When you integrate OpenMetadata, you can use Entity versioning to observe the changes through which metadata has gone through. Expect to find a myriad of details from small description changes to modifications of data types. For facilitated communications and control over updates, integration with Slack and Webhooks allows you to configure it in such a way to receive event notifications. 

Shipping ML products requires expertise and the right partners to assist you in building machine learning solutions. At Blue Orange Digital this is our daily job. Schedule a free consultation to learn more about our customized services.