Machine learning operationalization (MLOps)
Machine learning operationalization, also known as MLOps, is a process or methodology that is applied to tools and resources to ensure that machine learning projects are run properly and efficiently.
Updated: January 14, 2024
Machine Learning Operationalization, commonly known as MLOps, is a crucial methodology applied to tools and resources to ensure the proper and efficient execution of machine learning projects. This process establishes a culture and practice that aims to integrate the development and operation of machine learning systems seamlessly. Machine learning, a field where computers learn and make decisions without explicit programming, is instrumental in identifying patterns, making informed decisions, and predicting future outcomes.
In the realm of MLOps, various tools and software contribute to the operationalization process. Apart from dedicated AI and machine learning operationalization (MLOps) software, data labeling software also plays a significant role. These tools monitor and optimize models, and different degrees of these capabilities can be integrated into data science and machine learning platforms.
Machine learning operationalization can be broadly categorized into three main types: Data Management, Modeling, and Operationalization. This process involves transforming data into predictions, with MLOps acting as a navigational guide to keep track of the work and optimize various aspects of the process. To be effective, MLOps needs to be integrated into a company's broader data and machine learning initiatives. Key steps in the machine learning operationalization process include versioning the source data and its attributes, building and experimenting with models, deploying the model, and continuously monitoring for issues or anomalies, such as model drift or data drift.
Versioning source data is critical for maintaining a historical record and understanding changes over time. Building and experimenting with models involve iterative processes to enhance accuracy and efficiency. Deploying the model ensures its integration into real-world applications, and ongoing monitoring helps detect issues like model drift or data drift, which can impact performance. In essence, MLOps serves as a comprehensive framework for organizations to operationalize machine learning, providing structure and efficiency across the entire lifecycle of a machine learning project.