MLOps Development Process
1. Business Context and Stakeholder Inclusion
Any problem demands context from the business. A solution for one industry may not be applicable to another, nor is every business the same. Establishing shared context helps get the project off to the right start.
More than just identification, we find projects are optimal when key stakeholders are including in the project. With “skin in the game”, stakeholders are far more likely to collaborate to produce a better overall solution.
2. Problem Definition
A key phase where business problems are defined and prioritized. It is worth spending time to get this right, as subsequent effort could be ineffective and wasted.
3. Domain Knowledge Transfer
Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions.
4. Process Gap Analysis
In this phase our expert consultants speak to all the stakeholders and users to find the largest efficiency losses and pain points in the ML productionization process.
5. Research and Validation
Finally we get to the phase where we begin to design a solution, based upon the requirements for the MLOps system gathered earlier in the process. In some cases we also provide POCs to validate these solutions.
6. Agile Development and Implementation
Development and implementation phases usually split off into agile development flows. We usually collaborate with the wider engineering team and join their cadences.
7. Early Adopter Testing
As soon as the solution is stable enough for testing, we bring in the early adopters (often the stakeholders) to start using the system. Quickly we find the flaws and improve, before rolling out more widely.
8. Roll Out and Operation
Finally we roll out the solution to the rest of the users and organization. For some organizations we actively operate these systems too.