Tooling and infrastructure
AI applications into production, fast.
Autonomy and empowerment
The Peak platform allows efficient design and implementation of complex AI applications.
The orchestration layer in Peak means data scientists can spend their time focusing on the business logic in an application, not the intricacies of cloud infrastructure. Instead, data scientists can build, test, verify, experiment and collaborate in a flexible manner — at pace — resulting in an application that’s powerful, flexible and resilient to change.
Productionize AI models at scale
AI models fail to get into production due to a disconnect between builders and end users.
Workflows, on Peak, allows data scientists to develop, edit and troubleshoot applications with ease.
Workflows is a directed acyclic graph (DAG); this orchestration layer allows data scientists to visualize complex business problems and build their intelligence. Workflows has the flexibility to support mixed ETL scripts with code to rapidly build AI pipelines.
Peak: composability at its core
Applications are built using Press, our infrastructure-as-code framework and toolkit.
With Press, AI applications can be constructed efficiently and in a standardized way using Blocks. This gives us the tools to quickly tailor applications to your specific needs, by adding new Blocks or even creating custom Blocks. This helps us speed up delivery and time to value.
Easily access AI-ready data
Peak uses familiar workspaces like Jupyter Hub and RStudio that can be configured in one click, removing any MLOps requirement with data already connected in the back end.
These workspaces are highly flexible, with the ability to work in Python, R or any open source coding language.
