Build, deploy, and operate Enterprise AI applications.
Model Driven Architecture
What is AI Application Development
The BHC3™ AI Suite’s AI Application Development capabilities are designed for data science teams requiring uncompromised flexibility for experimentation, and unrestrained power, monitoring, and automation for ML operationalization. These capabilities provide a full view into the organization’s raw data sources and transformations using visual tools or a notebook. They can address high-complexity use cases using fully configurable and extendable templates for deep learning, natural language processing, CPU/GPU hardware selection, model auto-tuning, live production A/B testing, streaming prediction services, model life cycle management, multiple language support, composable ML Pipelines, continuous logging, CI/CD, and optimization.​
Third Party Support / Integrations
On-demand Jupyter Service
Use on-demand Jupyter environments running in configurable containers with open-source libraries from Conda, PIP, or CRAN​
- Use Jupyter Notebook to view data stored in BHC3 Models​
- Easily fetch instances of a BHC3 Model, and view as tables in a notebook​
- Readily visualize and plot time series data​
- Use your favorite libraries to visualize and explore data​
Multi-language SDK’s
Develop and run custom Python functions that are checked into your GitHub repository, tested using PyTest and CI/CD processes, then executed natively in a notebook or converted to an application API.​ Bring your own client in situations with remote connectivity and connect to all the BHC3 AI Suite data and services using Python and R SDKs.
Open Platform
Spend more time building better models when using state-of-the-art AI/ML technologies like Spark MLlib, TensorFlow, scikit-learn, cuDNN, SciPy, Caffe, PyTorch, AWS ML, Lex, Polly, Rekognition, Azure ML, H20.ai, Stanford Core NLP, and NLTK in one collaborative data science workspace built for the enterprise.
