If you are micro-services based architecture, then it is very important to track and log a request through the multi-service calls and relate the logs.
Syslog with a common UUID set at the entry point (typically at proxy or load balancer level e.g. nginx) of the request and then logger will log the UUID as the part of UUID parameter of Syslog.
Syslog then can be parsed and also form JSON objects for the poeple in Ops and send it to a service. Basically you could have one more internal micro-service to handle logs from Syslog and then parse it and send it wherever Ops would need.
I would avoid syslog. The protocol does not handle multiline messages (stack traces) etc. gracefully since it's purely line-based. It's also very hard to encode application specific fields into syslog. However, it's a good idea to pick (or design) something based on which fields traditional log daemons use - either based on syslog, or based on systemds journal. (which, btw. does not suffer from any multiline issues)
Along those lines (and possibly what the OP was looking for as well), what are the conventions around key names including namespacing? Or what are the common environment variables you capture for a given service, i.e. the boilerplate stuff.
+1, Request tracking across micro services is a must. You will be crying floods at the worst imaginable time if you do not have this. Think of it like a stack-trace for each request.
No, this is for a general introduction to the mathematics of computer science. This looks like a basic (and good!) text any MIT freshman should be able to master. Perhaps it's for what 6.001 has morphed into?
If you understand this stuff, you really need linear algebra for today's "deep learning", which perhaps is 18.03 (I can no longer remember).
I meant Course 6, which can be 6-1 (EE) or 6-2 (EECS). Given that there are many 6-2 majors out there who say they've majored in computer science, it might come as a surprise to others who don't understand the MIT curriculum that a "computer science" major at MIT does not need to take this class.
Not really. You're better off looking at introductory calculus and statistics. This book places more emphasis on discrete math.
One good way to go about it is to audit an online course [like Andrew Ng's] and figure out what gaps you need to fill in your knowledge to understand the material.
I am familiar with Linear algebra and at most average understanding of graph theory. I know there is lot more math to cover for ML but I find it overwhelming to start
All you need for introductory machine learning is multivariable calculus (for some simple optimization stuff), linear algebra, and probability. If you don't know probability, here's a good course: https://ocw.mit.edu/courses/electrical-engineering-and-compu....
In its current state, even cutting-edge machine learning is pretty accessible if you have a good understanding of linear algebra and calculus. If you want to do have a deeper understanding of machine learning then vector calculus, tensors, graph theory, etc. can only help you.
You're a godsend, thank you from another person. I've been slowly self working through textbooks my friends give me over the years after they finish from their classes but haven't really known which direction to go in being nontraditional.
Is there a recommended order to these? I skipped two years of math in High School and ended up BSing my way through Calculus without learning any of it, so I'm trying to figure out what I may need to fill the gaps
Yeah, we had a pre-tests for algebra & trig but I read the textbook before the class started and got 100% on the pre-tests, so they skipped me ahead. In retrospect I regret it.
I recently got accepted in to the Gigster network and I haven't received any new "gigs". They seem to have poor process for onboarding a newbie in terms of allocating projects. Most of the projects require people with Karma more than "350+" or "321+" - whatever that odd number means - (the default karma is 300). With more than 500 developers and designers in the network it is becoming more and more difficult for newbies to get any sort of project. Because the number of projects are not flooding in as the new members of developers
The probability keeps getting lower as new members join :|
how was the summary points done? is it through processing the text through ML or Reddit does provide with key summary points or some 3rd party service?
so much better for the web application world. Most of the support tickets / issues are mostly related to the browser they are using. This is one step closer to removing IE.\
