Machine learning operationalization (MLOps) platforms allow users to manage, monitor, and deploy machine learning models as they are integrated into business applications, automating deployment, tracking model health and accuracy, and enabling teams to scale machine learning across the organization for tangible business impact.
Core Capabilities of MLOps Platforms
To qualify for inclusion in the MLOps Platforms category, a product must:
- Offer a platform to monitor and manage machine learning models
- Allow users to integrate models into business applications across a company
- Track the health and performance of deployed machine learning models
- Provide a holistic management tool to better understand all models deployed across a business
Common Use Cases for MLOps Platforms
Data science and ML engineering teams use MLOps platforms to operationalize models and maintain their performance over time. Common use cases include:
- Automating the deployment pipeline for ML models built by data scientists into production applications
- Monitoring model drift, accuracy degradation, and performance anomalies in deployed models
- Managing experiment tracking, model versioning, and security governance across the ML lifecycle
How MLOps Platforms Differ from Other Tools
MLOps platforms focus on the maintenance and monitoring of deployed models rather than initial model development, distinguishing them from data science and machine learning platforms, which focus on model building and training. Some MLOps solutions offer centralized management of all models across the business in a single location, and may be language-agnostic or optimized for specific languages like Python or R.
Insights from G2 on MLOps Platforms
Based on category trends on G2, model monitoring and experiment tracking stand out as the most valued capabilities. Improved model reliability and faster iteration cycles stand out as primary benefits of adoption.
