Why and how to monitor machine learning models
This is the 41st edition of CrunchX and here are the stories and resources we thought were worth spending time on.
One of the most important tools in any software engineer’s toolbox is a good terminal and the ability to see a lot of information, presented clearly, on the screen. Whilst my own personal favorite (bring a macOS user) is iTerm2 with its tabs, I know many people who use tiling window managers inside terminals themselves. Terminator is a great example of this ‘genre’ and if you haven’t heard of these before or are not sure how they work then it’s well worth a look. Written by Anuj Sharma on It’s Foss and editorial selection by Dr. Stuart Woolley. Read the article here:
I’ve never programmed too much in Forth myself (but did dabble with the language once in the 1980s then again a few years ago) but I still know people that write some niche commercial software in it — primarily in embedded systems. Ray Duncan’s article “A Forth Apologia” is a snapshot in time, originally published in 1988, and offers a unique view into the history and philosophy of the language. If you’re a computer scientist and are interested in ‘could have been’ languages or even fancy trying it out then it’s an excellent read. Written by Ray Duncan on Holonforth and editorial selection by Dr. Stuart Woolley. Read the article here:
Companies are hiring Data Scientists for developing ML models and conducting AI experiments. This article features 5 capabilities that can be helpful when upgrading the data science productivity within an organization. Written by Isaac Sacolick on InfoWorld and editorial selection by Christianlauer. Read the article here:
It is not only important to develop machine learning models, one must also learn how to monitor and keep them reliable for further analysis. This article describes specifically why it is important and how to monitor machine learning models. Written by Isaac Sacolick on InfoWorld and editorial selection by Christianlauer. Read the article here:
Data can be found everywhere nowadays. Images can therefore also be good sources. This article features eight tools that help extract and manipulate data from pictures and images. Written by Vijay Singh Khatri on KDNuggets and editorial selection by Christianlauer. Read the article here:
It can be helpful for one’s portfolio when working with data that help solve real-world problems. This article describes three projects that just do that. Written by Nate Rosidi on KDNuggets and editorial selection by Christianlauer. Read the article here:
How many times did you miss the estimate? Most of the time this are just educated guesses. Read this story for the estimate laws. These should help ease the pain of estimates. Written by Maarten Dalmijn on Maarten’s Newsletter and editorial selection by Miloš Živković. Read the article here:
Every architecture has a cost. And what I like about this story is profiling code. Premature optimization is root of all evil. So use a profiler beforehand. Written and published by Kirill Rogovoy and editorial selection by Miloš Živković. Read the article here:
I’m always looking for a reason to avoid touching any Python code — for me it’s just a language that doesn’t suit any purpose well enough to be used. It’s interpreted, lacks any meaningful multi-threading capability, chokes on huge data sets, and who does fixed formatting in 2022 anyway?
To that end I’d always go back to my old faithful (and favourite) C for low level programming that requires speed, use Go simply for ease of use and speed at a higher level, and lately Julia as it’s a little bit of both… Jakob Nybo Nissen has two excellent blog posts about Julia, “What’s bad about Julia” and “What’s great about Julia” which really tell you all you need to know about why you should try dipping into the language if you haven’t already. Also, it’s fun — and you can’t say that about many modern languages! Written by Jakob Nybo Nissen on Viralinstruction and editorial selection by Dr. Stuart Woolley. Read the article here:
What’s bad about Julia: https://viralinstruction.com/posts/badjulia/
What’s great about Julia: https://viralinstruction.com/posts/goodjulia/