- Why is it Unique?
- BHC3 Integrated
- Data Integration
- AI/ML Development
- Operations and Security
- Deployment Options
Build, deploy, and operate Enterprise AI applications.
- 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
- 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
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 BHC3 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
BHC3 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 BHC3 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 BHC3 ML Studio or connect your existing client with Python and R SDKs
- 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 BHC3, or easily connect to a remote ML service over APIs