What is MLOps?
MLOps are derived from machine learning (ML) and operations (Ops). It is a set of management practices for the deep learning and production ML lifecycle. MLOps promotes communication and collaboration between operational professionals and data scientists. This approach aims to improve the quality of the production ML and increase automation by focusing on legal and business requirements.
MLOps covers the entire ML lifecycle, including the software development lifecycle and integration with the model generation, including continuous integration and deployment; effort; orchestration; management; health and diagnostic monitoring; and analysis of business statistics.
What is the MLOps lifecycle?
The MLOps lifecycle includes four key sub-cycles,
- The data cycle.
- The model cycle.
- The development cycle.
- The operations cycle.
Each cycle feeds information forward and backward.
![MLOps]()
Benefits of MLOps
MLOps can assist organizations in many ways,
- Scaling: MLOps is critical to scaling an organization’s number of machine learning-driven applications.
- Trust: MLOps also builds trust for managing machine learning in dynamic environments by creating a repeatable process through automation, testing, and validation.
- Better use of data: MLOps can radically change how businesses manage and capitalize on big data.
- Seamless integration, improved communication
- Compliance: The regulatory and compliance piece of operations is an increasingly important function, particularly as ML becomes more common.
- Reduced risk and bias: Business risk via undermined or lost consumer trust can be the result of unreliable, inaccurate models. MLOps can help prevent development biases leading to missed opportunities, underrepresented audiences, or legal risk.
Summary
MLOps enhances the reliability, credibility, and productivity of ML development. By improving products with each iteration, MLOps shortens production life cycles, driving reliable insights that can be used more rapidly.
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