Optimize MLOps With AI Observability
What is MLOps?
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.
Why is MLOps Important?
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.
MLOps for the Entire ML Lifecycle
AI Observability 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.
Your Partner for MLOps
Continuous Monitoring
- Centralized view of all models for MLOps, ML engineers, data scientists and line of business to collaborate throughout the ML lifecycle.
- Observe model health by tracking performance, drift, data integrity or model bias.
- Monitor structured and unstructured (text and image) models.
- Detect behavior changes in models with highly imbalanced datasets.
- Receive real-time alerts on model performance, data drift and data integrity.
Deep Explainability
- Enable stakeholders and regulators with human-readable explanations to understand model behavior.
- Obtain global and local-level explanations of how different attributions contribute to model prediction.
- Compare and contrast feature values and their impact on model prediction.
- Bring your own explainers into Fiddler for explanations customized for your AI projects.
Rich Analytics
- Validate model before deployment with out-of-the-box performance metrics.
- Uncover underlying reasons causing model performance or model drift issues with root cause analysis.
- Drill-down on slices of data to understand underperforming segments.
- Improve models with actionable insights from powerful dashboards showing feature impact, correlation or distribution.
Trust and Fairness
- Assess models with model and dataset fairness checks at any point in the ML lifecycle.
- Increase confidence in model outcomes by detecting intersectional unfairness and algorithmic biases.
- Leverage Fiddler’s out-of-the-box fairness metrics including disparate impact, group benefit, equal opportunity and demographic parity.
Model Governance
- Evaluate model performance and ensure all models are compliant for audit purposes
- Provide stakeholders with fine-grained control and visibility into models, enabling them with deep model interpretations
- Reduce risks from model degradation or model bias