Machine learning operations (MLOps) is a blend of cultural practices involving data and its usage, data science, and tools that help ML teams rapidly iterate through model versions or run experiments to test different hypotheses. MLOps best practices help ML engineers, data scientists and DevOps engineers break down silos to collaboratively streamline the ML production process, from model training to deployment to continuous monitoring, ensuring the quality of AI solutions through effective model governance.
Adopting MLOps is imperative for ML teams to efficiently operationalize the ML lifecycle and align model outcomes to meet business needs.
MLOps is the equivalent of DevOps for machine learning. An MLOps framework includes many widely-adopted engineering practices and philosophies from DevOps to bring models into production, continuously monitor models, and meet compliance requirements like AI regulations.
Due to complexity in the ML lifecycle, MLOps includes ML-specific practices in addition to DevOps best practices. ML teams need continuous visibility into what their ML is doing in production, understanding why predictions are made, with control points to refine and react to changes.
The complexity in ML stems from changes in model behaviors influenced by changes in structured and unstructured data over time.
MLOps teams monitor the quality of model prediction to ensure models are working properly. When model degradation or drift is detected, ML teams perform root cause analysis to further understand model behavior. They gain actionable insights from deep explainability on pattern changes or model bias to create a feedback loop to improve models throughout their lifecycle.
Model Performance Management (MPM) is the foundation of good MLOps practices, enabling you to gain full visibility of your models in each stage of the ML lifecycle from training to production.
The Fiddler MPM platform supports each stage of your MLOps lifecycle. Quickly monitor, explain, and analyze model behaviors and improve model outcomes.
Build trust into your AI solutions. Increase model interpretability and gain greater transparency and visibility on model outcomes with responsible AI.
Increase positive business outcomes with streamlined collaboration and processes across teams to deliver high-performing AI solutions.