AI/ML has the potential to deliver incredible value to customers. A single breakthrough can make a product company's growth soar. A fine-tuned model can completely transform an enterprise's operations.
The mechanics of delivering such a program are very similar to traditional projects on the face of it, so it is tempting to think of the implementation as being the easy part. But because of the non-deterministic nature of AI/ML, all the pitfalls of ordinary software development programs - misaligned objectives, underestimation, lack of process, skimping on QA, ignoring risks, etc. - are amplified x10. To deliver successfully, you need to ratchet up the program diligence.
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 From research and prototyping to commercial development, AI/ML requires making decisions and trade-off's. This is where the PM needs to provide loads of context to the R&D team: what are the customer problem(s) are that we are trying to solve? What parameters and trade-off's would be acceptable to a customer? Have we considered the cost trade-offs of operating in practice?
There is a strong and immediate demand for Data Science in all industries, but a limited supply of qualified data scientists. There is also a fuzzy understanding of how data science actually works. Data science is "black magic" to most managers. These factors result in companies paying a high premium for data scientists today.
In such a competitive job market, how do you build your own AI / ML / data science team? |