C3 AI ML Studio
C3 AI ML Studio is a low-code / no-code collaborative development environment where data scientists can rapidly design, train, and deploy AI / ML algorithms at scale across the enterprise. C3 AI ML Studio is part of the C3 AI Integrated Development Studio (IDS), placing data science work at the core of the AI application development lifecycle, with access to all upstream and downstream workflows like data management and application user interface development.
- Develop and deploy AI and machine learning algorithms across an end to end low-code / no-code machine learning workflow
- Accelerate machine learning experimentation, development, deployment, and tracking
- Enhance collaboration across data science teams
On-Demand Jupyter Notebook service and SDK connectivity
Connect to Jupyter Notebook with one-click and operate on auto-scaling clusters, allowing maximum performance at minimal cost
- Access all authorized data, APIs, and ML services (such as pre-built ML pipelines) on the notebooks using Python and R SDKs
- Prototype new functions or extend existing services to make your code and new services available across teams
- Flexibly connect your own remote client, choose your own open-source libraries, or access a custom package repository to tailor the service to meet your needs
Runtimes and resource profiles
- Install leading open-source libraries for Python and R in Jupyter Notebooks or custom functions
- Connect public repo from Conda, PIP, CRAN, or a private repository exclusive to your organization
- Configure resource requirements to optimize performance, such as resizing a Jupyter Notebook service, or specifying custom CPU or GPU workloads
Auto ML
- Configure and run hyperparameter optimization experiments, initialized from Jupyter or an event-based trigger
- Keep track of the team’s experiments on the Experiments page to discover the best settings and minimize duplication of efforts
- Inspect experiment progress and results with interactive charts like parallel coordinates
Model performance tracking
- Monitor and compare 100s of thousands of models on one environment to discover the best solution
- Automate production routines such as drift detection, alerting, and retraining based on key performance metrics
Multi-model Deployment
Use end to end ML Ops workflow to rapidly develop, test, and operationalize AI / ML algorithms while enabling collaboration across data science teams.
- Build, train, test, deploy, and operate models in production or experimental applications
- Develop models in no-code or code-based interfaces using C3 AI ML Studio’s prebuilt templates or custom code
- Promote leading models into production and manage with automated operational services
- Use auto-scaling to allocate flexible compute and memory resources for individual users
- Explore data, develop machine learning features, and author models with any Python or R library available through Conda, PIP, or CRAN
Third-party support
C3 AI ML Studio includes extensive third-party support that extends to the tools and ML libraries and frameworks that data scientists use on a day-to-day basis. With C3 AI ML Studio, teams spend less time getting disparate tools to work together, and more time building better models with many pre-integrated state-of-the-art AI/ML technologies.
- Use on-demand Jupyter notebooks in C3 AI ML Studio or connect your existing client with Python and R SDKs
- Centrally manage custom runtime environment definitions that include the language (Python, R, JavaScript), language version, package repository, libraries, and library version, then use the same runtime during prototyping and operation phases
- Leverage leading open-source libraries pre-packed in composable ML Pipeline objects that can be used out-of-the-box or custom-configured with your own libraries
- Export and run third-party models in C3 AI, or easily connect to a remote ML service over APIs
