Teams & technology
Lean AI is a framework focusing on incremental progress.
The requirements and solutions evolve through the collaborative effort of self-organizing and cross-functional teams and the business objective of an organization. It advocates adaptive planning, evolutionary development, empirical knowledge, and continuous improvement, and it encourages rapid and flexible response to change.
With this approach you'll be able to cope with uncertainty, minimise risks and quickly identify opportunities.

It is inspired by other development paradigms and frameworks but carefully designed for the specifics of the model development life cycle needed for machine learning.

It is a structured and easy to follow process for taking AI to production with the goal of delivering business value quickly and efficiently.
The Lean AI cycle
The starting point for the Lean AI process is classifying the business problem and understanding the quality and amount of the data available.

The goal of Lean AI is to get a first baseline model or minimal implementation in production.
Developers and data scientists need a fully configured environment with the right setup of hardware and software before the actual training takes place. The team takes time at the beginning of a project to think ahead to production deployment and engineer a robust architecture that will allow us to accomplish our goals together.

After the initial step of validating the use case, time scale, scope, data acquisition and a framework for ML training the next priority is to get a first baseline model or minimal implementation.
ML engineering
To bootstrap the Lean AI cycle you should start to build up enough of the system so that the performance can be evaluated and start iterating.

This typically means collection and data analysis to establish training, development and testing datasets, and getting a simple model online. The goal is not to solve the project in one go, but to start our iteration cycle.
We should have shippable models at every stage so that even in the simplest form we have something we can put into production.
The ultimate goal of the Lean AI process is getting a model into production and think about deployment from the start. It's a collaborative effort with the software and operations teams, as well as other key stakeholders, to understand the key integration points, as well as the volume and velocity of data and predictions so that we can set up clean interfaces for upstream and downstream integrations.