It empowers the enterprise’s operations by identifying points of automation and AI infusion and deploying machine learning tools to enhance the production pipeline. With MLOps, machine learning projects are reliable, scalable, replicable across the enterprise, and keep getting sharper with each iteration.
At Nuvento, our MLOps services comprise a comprehensive suite of tools, processes, and best practices designed to streamline the machine learning lifecycle and enhance the innovation prowess of your enterprise.
Services like Git provide robust version control for machine learning code and models. This facilitates collaboration, change tracking, and the management of different model versions.
CI/CD pipelines automate the testing, building, and deployment of models. Tools like Jenkins, GitLab CI/CD, and Travis CI ensure consistent and reliable deployment of updated models.
Docker and Kubernetes are instrumental in packaging models and their dependencies into containers. This ensures consistent deployment across diverse environments and simplifies scaling.
We offer a central repository to store and manage different model versions, simplifying change tracking and access for deployment. Services like MLflow and DVC provide such capabilities.
Our services, including FeatureHub and custom solutions, enable seamless management and control of features used by models in production. This allows for easy feature toggling and experimentation.
Real-time model performance monitoring is facilitated by tools like Prometheus and Grafana. This helps in detecting anomalies and ensuring that models perform as expected in production.
Automated testing is a core component of our MLOps strategy. We validate model behavior and performance through unit tests, integration tests, and tests for drift detection.
Tools like Terraform and CloudFormation automate the provisioning of infrastructure resources required for model deployment and execution, ensuring consistency across environments.
Kubernetes, among other services, enables dynamic scaling of deployed models based on demand. This optimizes resource allocation and enhances cost efficiency.
Our MLOps services encompass security practices such as model and data encryption, access controls, and compliance with data protection regulations like GDPR.
We emphasize the importance of regularly updating and retraining models to maintain accuracy. Automated pipelines can be established to trigger model updates when new data becomes available.
MLOps encourages the incorporation of user feedback and model performance data to continuously enhance models over time.
Services like Confluence, Notion, or Wiki systems are used to document model architecture, data pipelines, and deployment processes, fostering better collaboration and knowledge sharing.
Our MLOps services facilitate systematic testing of various model configurations and variations to optimize performance through techniques like A/B testing.
