Successful adoption of powerful data-driven AI technologies does not start with a massive transformation of the core organisation or in large project silos. Rather, it's done by adopting one use case at a time, harvest real value from each use case and incrementally build out the knowledge and skills needed to move AI into other business areas by learning to work in an agile process across different roles and teams in the organisation.
- Enables an agile/lean approach to AI projects, built with the aim to help cross-functional and full-stack teams deliver production solutions rapidly.
- Machine learning framework agnostic. Supports most machine learning frameworks out of the box and with open APIs support extension for additional frameworks.
- Continuous analytics. Support for automating pipelines from data ingestion to model deployment and monitoring.
- Workflow orchestration. Automate your workflows to handle tasks such as grid searches or active learning pipelines.
- Simple deployment. Run anywhere you can run Kubernetes, hosted on-prem, in the cloud or on your laptop.
All projects aims to transfer knowledge critical for a holistic end-to-end view of the full stack data science process. And after the project Scaleout offer professional services to help you continue developing confidence and fluency with the most powerful technology tools.