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Solution architecture is the process of designing machine learning systems and applications against identified objectives, constraints, and requirements.
In the context of machine learning and artificial intelligence, a structured solution architecture process allows for the proper scoping of desired projects, de-risking of the engagements against data availability and stakeholder inputs, and assurance towards all target outcomes.
There are many advantages to a solution architecture-based approach to machine learning, including:
Stage-gating. Deployments can be prototyped and tested internally before going live.
Sequential Deployments. Deployments can be developed and optimized against data maturity and infrastructure costs, resulting in efficient and effective solutions.
Multiple approaches to clear objectives. For every defined objective, there may be multiple machine learning approaches to accomplish them. A well-structured process will narrow down the best approaches.
A good process will help you learn about the problem as you develop the solution. A great process will provide a learning experience to the rest of the organization.
Our process of presenting solutions to clients blends design thinking, deep technical knowledge, and multiple points of view to ensure complete coverage. By spending more time understanding your organization's needs and priorities, our solutions can align with your key performance indicators.
It’s not about finding the best models, it’s about finding the correct models–for you.
