Riemann VS deep-diamond

This is fine- we do something similar using riemann.

"It can't go into infinite loop" is utterly irrelevant. Over last maybe 15 years I've used a bunch of apps that just used their own programming language (from simple DSL to "just write exactly how the app is supposed to handle data") and literally not a single time has that become a problem.

Metrics are measurements of something about your system. They are numeric values, over an interval of time, usually with associated metadata (e.g., timestamp, name). They can be raw, calculated, or aggregated over a period of time. They can come from a variety of sources like servers or APIs. Metrics are structured by default and can be stored in open source systems like Prometheus and Riemann or in off-the-shelf solutions like Amazon CloudWatch and Azure Monitor. These optimized storage systems allow you to perform queries, create alerts, and store them for long periods of time.

Reason #1 - Riemann https://riemann.io/

Riemann is a tool for distributed system monitoring. It aggregates events from user servers and applications, combines them into a stream and transmits them for further processing or storage. Greater flexibility and fault-tolerance make Riemann different from other similar systems. Moreover, it’s written in Clojure almost completely. The code is available on GitHub and is distributed under Eclipse Public License 1.0.

I did a new release today of Mirabelle, a stream procesing tool heavily inspired by Riemann. I also spent a lot of time on the documentation website if you want to try it, and also wrote an article today about an use case.

Consider dabbling in a project to get your feet wet first. You have a neat problem you want solved? Give it a shot. There an interesting open source project, fork it and tinker with the code. This will be tremendously educational both vocationally and will help you get a feel for if you'd like to work in clojure all the time. There are a lot of projects, but I chose https://github.com/riemann/riemann to read and try better to understand real world clojure.

I had some "classical ML" knowledge and knew a bit about the math behind DL and tensors in general thanks to the book Deep Learning for Programmers showcased in this repo: https://github.com/uncomplicate/deep-diamond (it's not in Rust, and I'm not sure what the current state of it is, though!).

Plase check out clojars, or https://neanderthal.uncomplicate.org/ https://clojurecuda.uncomplicate.org/ https://github.com/uncomplicate/deep-diamond

Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.