Managing large-scale knowledge science and machine studying initiatives is difficult as a result of they differ considerably from software program engineering. Since we goal to find patterns in knowledge with out explicitly coding them, there’s extra uncertainty concerned, which might result in varied points reminiscent of:
- Stakeholders’ excessive expectations could go unmet
- Initiatives can take longer than initially deliberate
The uncertainty arising from ML initiatives is main explanation for setbacks. And relating to large-scale initiatives — that usually have larger expectations connected to them — these setbacks could be amplified and have catastrophic penalties for organizations and groups.
This weblog publish was born after my expertise managing large-scale knowledge science initiatives with DareData. I’ve had the chance to handle numerous initiatives throughout varied industries, collaborating with gifted groups who’ve contributed to my progress and success alongside the way in which — its due to them that I might collect the following pointers and lay them out in writing.
Beneath are some core rules which have guided me in making a lot of my initiatives profitable. I hope you discover them priceless…
