Machine Learning vs Distributed Systems
I love both the subjects and having difficulty to pick one. The former is becoming a necessity while later is specialized making it more interesting. Please advise on job prospects and challenges
Having worked on both these areas for the last 5-6 years I can add my 2 cents . I will say go with something which interests you the most . You can really go deep in the area of ML and learn algorithms and implementations in depth and to scale the algorithms there are now widely used frameworks - tensorflow ,pytoech , mxnet for distributes NN , for scikitearn based models you can use dask ml and rise which just came out to parallelise. Also you can use spark MLlib for the ones supported by spark The data engineering and distributed systems are a beast in itself , where you can and dedicate a lot of time on Hadoop ecosystem , hbase , Cassandra , hive and also real time systems like kafka, spark , storm etc . Now each of these components can be used , put together depending on the use cases and learning the distributes systems does not only mean knowing the basics of these subsystems but to learn what fails when things go wrong and how to debug , drill down into pipelines and fix them Overall it depends on your areas of interest and I feel picking one of these areas as a exploratory path may be better to do justice
Thank you, it’s more than 2 cents
is this jumping field while in profession or as a student?
I’m a backend engineer. I do 40 hrs a week, I have a lot of free time to focus on one of these subjects