MLOps vs DevOps

Machine learning operations (MLOps) differ from development operations (DevOps) in several key areas, and Domino supports these differences with key capabilities that are optimized to maximize flexibility for data scientists while ensuring control and governance for leaders and information technology (IT) professionals.

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Exploration precedes development and operations

In traditional DevOps, the process typically begins with a clear definition of requirements and a direct move to development. In contrast, MLOps often starts with an exploratory phase where data scientists analyze data to validate hypotheses and understand underlying patterns before formalizing a solution. Domino provides an interactive, collaborative workspace where data scientists can easily explore and visualize data without cumbersome setup. The platform supports rapid prototyping and iterative analysis, enabling teams to quickly and effectively experiment with different models. With Domino, exploratory work is seamlessly integrated with later stages of model development and deployment, thanks to the platform’s ability to manage the entire lifecycle within a unified environment.

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The data science lifecycle requires an adaptive way of working

Unlike DevOps, where processes can be linear and predictable, the data science lifecycle is inherently iterative and experimental as it requires frequent adjustments based on new findings and evolving data insights. Domino accommodates the adaptive nature of data science projects by facilitating easy iteration of models and experiments. The platform’s project management tools allow for flexible adjustments and integrations with other services, ensuring that data scientists can adapt their workflows as needed without losing track of changes or performance metrics.

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Limits on data quality and availability limit progress

DevOps relies on stable, predictable software development environments, while MLOps must often contend with variable data quality and availability, which can significantly impact model performance and development timelines. Domino tackles these challenges by providing robust data management tools that enhance data quality and ensure reliable access to datasets. The platform supports versioning of data alongside code, enabling teams to maintain consistency across experiments and to easily roll back to earlier states if new data adversely affects model performance.

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A greater operational effort is required than in DevOps

MLOps requires more operational oversight due to the ongoing need to monitor and adjust machine learning (ML) models based on continuous data inputs, unlike traditional software which requires less monitoring after deployment. Domino offers comprehensive monitoring tools that track model performance in real-time, alerting teams to issues like data drift or performance degradation. The platform also automates many operational tasks, such as model retraining and redeployment, reducing the manual effort required to maintain high model accuracy and reliability.

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ML is a team sport and requires collaboration

While DevOps teams often comprise software developers and IT professionals, MLOps teams need a broader range of expertise, including data scientists, ML engineers, and domain-specific experts. Domino enhances collaboration across diverse teams by providing tools tailored to each role’s needs. For example, data scientists can focus on model building and testing, while ML engineers can utilize Domino’s deployment and monitoring features. The platform also facilitates knowledge sharing and best practices through integrated documentation and project management features, ensuring that all team members are aligned and the project is effectively managed.

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Next steps

Develop and deploy with pipelines.