DeepGenius AI DevOps
We enable you to apply DevOps principles to Explainable Machine Learning systems with our AI DevOps solutions.
AI DevOps Features and Benefits
Practicing AI DevOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.
ML pipeline automation
Perform continuous training of the model by automating the ML pipeline; this lets you achieve continuous delivery of model prediction service. To automate the process of using new data to retrain models in production, you need to introduce automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management.
CI/CD pipeline automation
For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.
AI DevOps setup
AI DevOps setup includes the following components: Source control, Test and build services, Deployment services, Model registry, Feature store, ML metadata store, ML pipeline orchestrator.
Get Started With DeepGenius AI
Perform Integration of all your data, Build AI models and Deploy critical AI applications and services. The possibilities are endless. Smart solutions are at the core of all that we do at DeepGenius AI. Get in touch with us to learn more about what we offer and get started with your AI journey.