Do these questions arise for you as a manager or specialist of an ML team?
• How to help newcomers get up to speed with the processes adopted by the team faster?
• How to consolidate all instructions, standards, rules, recommendations, useful and dangerous practices so that at least someone reads them?
• Is it possible to ensure uniformity in different teams' projects without suffocating them with rules?
• How to share the results of work with another team without unnecessary questions?
If so, this post is for you. And that also means that your team needs an ML process.
An ML process is a meta-instruction that becomes an "entry point" for finding answers to questions about project development. It has the following properties:
• Contains all necessary links to resources for development.
• Explains what to do and what not to do.
• Has a structure that repeats the development process with a comprehensive and up-to-date description of all stages.
• Becomes the basis for creating future project documentation
• Suitable for 95% of processes.
• Simplifies life, rather than imposing restrictions.
An ML process should not and cannot be written by one person, otherwise no one will use it. It's worth gathering a working group from different teams and creating a solution that will be useful and understandable to everyone.
What you will get if you work on an ML process?
• You will see "blind spots" in development and write new instructions that were lacking.
• You will gather in one place a navigator for all resources, tools, and instructions adopted in your team.
• You will facilitate the onboarding of newcomers in your team, the transfer of projects to colleagues, and the understanding of results by the manager.
• You will reduce the time spent on project development.
In the next post, we will provide a template for an ML process, which we use to collect project documentation.
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🤖 Data & ML | @DataXplore