My friend JS left the hospital architect job and went to some smaller firm, then to Nokia. After Nokia was acquired by Microsoft he stayed for a while then moved to the current employer, a health-care related big-data startup. In his current architect role, he finds the technical challenges too low so he is also looking for new opportunities.
JS has been a big-data architect for a few years (current job 2Y+ and perhaps earlier jobs). He shared many personal insights on this domain. His current technical expertise include noSQL, Hadoop/Spark and other unnamed technologies.
He also used various machine-learning software packages, either open-sourced or in-house, but when I asked him for any package names, he cautioned me that there’s probably no need to research on any one of them. I get the impression that the number of software tools in machine-learning is rather high and there’s yet an emerging consensus. There’s presumably not yet some consolidation among the products. If that’s the case, then learning a few well-known machine-learning tools won’t enable us to add more value to a new team using another machine-learning tool. I feel these are the signs of an nascent “cottage industry” in the early formative phase, before some much-needed consolidations and consensus-building among the competing vendors. The value proposition of machine-learning is proven, but the technologies are still evolving rapidly. In one word — churning.
If one were to switch career and invest oneself into machine-learning, there’s a lot of constant learning required (more than in my current domain). The accumulation of knowledge and insight is lower due to the churn. Job security is also affected by the churn.
Bright young people are drawn into new technologies such as AI, machine-learning, big data, and less drawn into “my current domain” — core java, core c++, SQL, script-based batch processing… With the new technologies, Since I can’t effectively accumulate my insight(and value-add), I am less able to compete with the bright young techies.
I still doubt how much value-add by machine-learning and big data technologies, in a typical set-up. I feel 1% of the use-cases have high value-add, but the other use cases are embarrassingly trivial when you actually look into it. I guess it mostly consist of
- * collecting lots of data
- * store in SQL or noSQL, perhaps on a grid or “cloud”
- * run clever queries to look for patterns — data mining
See https://bintanvictor.wordpress.com/2017/11/12/data-mining-vs-big-data/. Such a set-up has been around for 20 years, long before big-data became popular. What’s new in the last 10 years probably include
- – new technologies to process unstructured data. (Requires human intelligence or AI)
- – new technologies to store the data
- – new technologies to run query against the data store