xpresso Control Center
Centralized Management Console
Enable team collaboration with reusable components, utilities, libraries and automated tools; ensure seamless management of the development, deployment, and post-production feedback loop of data science solutions
Manage End-To-End AI Development Lifecycle
- Create projects and workflows for data scientists and data engineers working on AI/ML projects
- Streamline the process of creating machine learning pipelines, data processing pipelines
- Integrate with existing applications and business processes
Leverage Best-Of-Breed Productivity Tools & Technologies
- Facilitate team collaboration with Bitbucket, JupyterHub and Gitlab
- Perform exploratory data analysis through Matplotlib, Seaborn, etc.
- Support all machine learning programming languages including Python, R, etc.
- Enable distributed computing systems like Spark, Hive and ElasticSearch
Automated MLOps With Distributed Training And DevOps Capabilities
- Track and manage training experiments along with metrics in real time
- Trace model lineage with data, code, model and parameters
- Deploy model into production with automated CI/CD/CT
