AWS Lambda Programming Language Comparison

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Now that AWS Lambda has added PowerShell to its growing list of supported languages, let’s take a moment to compare and contrast the different languages available to us.

In this post, we’ll take a look at these languages from a number of angles:

  • Cold start performance: performance during a cold start
  • Warm performance: performance after the initial cold start
  • Cost: does it cost you more to run functions in one language over another? If so, why?
  • Ecosystem: libraries, deployment tooling, etc.
  • Platform support: is the language supported by other function-as-a-service (FAAS) platforms?

We will also talk about specialized use cases such as Machine Learning (ML) as well as paying attention to the special needs of the enterprise. Finally, we’ll round off the discussion by looking at a few languages that are not officially supported but that you can use with Lambda via shims.

I should stress that the goal of this post is to consider the relative strengths and weaknesses of each language within the specific context of AWS Lambda. This is not a general purpose language comparison!

https://blog.epsagon.com/aws-lambda-programming-language-comparison

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190 universities just launched 600 free online courses. Here’s the full list

If you haven’t heard, universities around the world are offering their courses online for free (or at least partially free). These courses are collectively called MOOCs or Massive Open Online Courses.

In the past six years or so, over 800 universities have created more than 10,000 of these MOOCs. And I’ve been keeping track of these MOOCs the entire time over at Class Central, ever since they rose to prominence.

In the past four months alone, 190 universities have announced 600 such free online courses. I’ve compiled a list of them and categorized them according to the following subjects: Computer Science, Mathematics, Programming, Data Science, Humanities, Social Sciences, Education & Teaching, Health & Medicine, Business, Personal Development, Engineering, Art & Design, and finally Science.

If you have trouble figuring out how to signup for Coursera courses for free, don’t worry — here’s an article on how to do that, too.

Many of these are completely self-paced, so you can start taking them at your convenience.

https://qz.com/1437623/600-free-online-courses-you-can-take-from-universities-worldwide/

Practical Text Classification With Python and Keras

Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you’ll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text.

Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this?

https://realpython.com/python-keras-text-classification/

Making an Unlimited Number of Requests with Python aiohttp + pypeln

This post is a continuation on the works of Paweł Miech’s Making 1 million requests with python-aiohttp and Andy Balaam’s Making 100 million requests with Python aiohttp. I will be trying to reproduce the setup on Andy’s blog with some minor modifications due to API changes in the aiohttp library, you should definitely read his blog, but I’ll give a recap.

UPDATE: Since Andy’s original post, aiohttp introduced another API change which limited the total number of simultaneous requests to 100 by default. I’ve updated the code shown here to remove this limit and increased the number of total requests to compensate. Apart from that, the analysis remains the same.

https://medium.com/@cgarciae/making-an-infinite-number-of-requests-with-python-aiohttp-pypeln-3a552b97dc95

Top 10 Must-Watch PyCon Talks

Primer on Python Decorators

In this introductory tutorial, we’ll look at what decorators are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions.

By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

Sounds confusing—but it’s really not, especially after we go over a number of examples. You can find all the examples from this article here.

https://realpython.com/primer-on-python-decorators/

4 Techniques for Testing Python Command-Line (CLI) Apps

You’ve just finished building your first Python command-line app. Or maybe your second or third. You’ve been learning Python for a while, and now you’re ready to build something bigger and more complex, but still runnable on a command-line. Or you are used to building and testing web applications or desktop apps with a GUI, but now are starting to build CLI applications.

In all these situations and more, you will need to learn and get comfortable with the various methods for testing a Python CLI application.

While the tooling choices can be intimidating, the main thing to keep in mind is that you’re just comparing the outputs your code generates to the outputs you expect. Everything follows from that.

In this tutorial you’ll learn four hands-on techniques for testing Python command-line apps:

  • “Lo-Fi” debugging with print()
  • Using a visual Python debugger
  • Unit testing with pytest and mocks
  • Integration testing

https://realpython.com/python-cli-testing/