It is hard to build production systems that rely on bleeding-edge technology. The technology stacks frequently change and the goal is often not a static product but rather one that adapts rapidly to accommodate new tools and data.
There are four important guiding principles of an AI strategy:
- Reduce risk with an open-source centric AI strategy. By ensuring that you have continuous access to leading data engineers and cloud native experts that live and breathe open source.
- Enable a quick project start and an agile approach. There is no need to decide on a particular cloud infrastructure or a firm technological roadmap before getting started with your project. It is important to arrive at a sustainable solution no matter where the evolving requirements take you.
- Retain ownership of your AI projects. Being assured that you have continuous access to experts and engineering capacity to overcome technological roadblocks, even resource- and knowledge constrained teams can take full ownership of AI projects.
- Avoid vendor lock-in. The strategic choice of cloud infrastructure providers and data-asset ownership should not be taken on the basis of availability of particular ML tools. Sustainable AI is built with a cloud native strategy to ensure that your AI solutions are vendor agnostic.
The aim of Lean AI is to establish processes and technology that integrate knowledge, processes and data in machine learning workflows supporting continuous innovation and the guiding principles. Lean AI is designed to accelerate the adoption of AI by empowering the teams that take use cases to production services. It addresses the challenge of putting and keeping AI in production at its core by providing teams with knowledge, tools and support needed to repeatedly succeed with AI projects and ultimately delivering business value quickly and efficiently.
Deliverables & Master Class material
Master Class participants and prerequisites
Participants should have good business knowledge and be subject matter experts.
- Current or future participants in machine learning projects – domain experts
- Software Developers
- Data Scientists
- IT operations
software, Regulatory and Compliance tracking systems, Large Scale POS solutions for retail and food companies.