Learn how to deploy your ML models into production with confidence by load testing your APIs in Python with Flask and Locust

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Deploying with confidence

There are a number of ways to allay these anxieties. One approach is (on paper) straightforward: have a robust testing strategy. At the end of the day, an effective testing strategy…


Applying Data Science to Data Science: a deep dive into the best-loved technologies in the world of Data Science in 2020.

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Digging in

This article is broken into two parts:

  1. Technology — This section takes a deep-dive into the technologies that the Data Science world has been writing about and responding to this year. …


Getting Started

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Getting abstract

Arguably at the top of this pyramid of abstraction is the concept of serverless computing, and it is built on the idea that (as the name suggests), developers need not…


Data scientists: choosing whether to join a startup is a big decision. Here’s some advice to help you figure out whether life at a startup is for you.

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This post is (as the title might suggest) inspired by Chip Huyen’s ‘7 reasons not to join a startup and 1 reason to’ post. While reading it, I was reminded of several conversations I’ve had over the last year or so with various ML practitioners inside and outside of startups on why I think people should join a startup. Many of the topics in Chip’s post came up in those discussions, though perspectives regularly differed. I also realised I hadn’t taken the time to lock-in my own ideas on the topic. In other words: I was inspired to get my…


Getting APIs into production can be a bewildering prospect. This post gives you tips and a template for using Flask in production.

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What is Flask?

So why another Flask blog post? Over…


Getting Started

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Deployment is hard

That’s where automation can come in very handy: careful automation of ML pipelines can massively boost your productivity by allowing you to rapidly iterate on a pipeline in order to account for new…


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In Part 1 of this series, you saw a few practical examples of how Object-Oriented Programming (OOP) can be used to help you resolve some code design problems. If you missed it, it’s over here:

Right, let’s dig in.

Getting technical

  • Classes — The definition of the data and procedures available to a given structure. In other…

Creating CLIs can help improve accessibility and reuse of your ML pipelines, but they can be a pain to set up. Enter Fire.

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What is Fire?

So, why write a CLI? Practically, a simple CLI can make configuring a script as simple as changing a couple of command line arguments. Let’s say you’ve got a script set up on an orchestration service (maybe something like Jenkins)…


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What is Streamlit?

With this relatively mature ecosystem in place, you may question the need for a yet another framework to join the…


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The power of OOP

However, it isn’t uncommon for many programmers to swirl around concepts like OOP for many years — perhaps gaining the odd bit of insight here and there — but not consolidating that understanding into a clear set of ideas. For beginners too, the concepts of OOP can…

Mark Douthwaite

Applied AI specialist, computer scientist, software engineer. Read more at https://mark.douthwaite.io/

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