Term
MLOps (ˈɛmˌɛlˌɒps)
Definition
MLOps is an approach in AI that merges machine learning (ML) with DevOps practices to build, deploy, and manage machine learning models effectively.
Where you’ll find it
MLOps is not tied to a specific page or menu within AI platforms. It is a methodology applied throughout the project lifecycle, especially when managing AI models in production environments.
Common use cases
- Automating the machine learning model lifecycle to ensure it moves smoothly from creation to deployment and maintenance.
- Streamlining the update process for AI models, ensuring they adapt and improve without lengthy downtime or manual oversight.
- Facilitating close collaboration between teams managing data, coding, and deployment to enhance overall project efficiency.
Things to watch out for
- MLOps requires a solid understanding of both machine learning and software deployment workflows. A lack of expertise in either can complicate the process.
- It might need adjustments to fit specific project requirements or integration into existing workflows, which can require additional setup time.
- Keeping up with continuous integration and deployment may overwhelm teams new to DevOps culture.
Related terms
- Continuous Integration
- Continuous Deployment
- Machine Learning
- DevOps
- Production Environment
Pixelhaze Tip: Start integrating MLOps practices in smaller projects to build familiarity with the workflow before applying it to larger scale deployments. This helps in smoothing out any kinks in the process and ensures your team is comfortable with advanced operations when scaling up.