A Gentle Introduction: Automating Machine Learning Pipelines

A Gentle Introduction: Automating Machine Learning Pipelines

A Gentle Introduction: Automating Machine Learning Pipelines. This post shows you how to automate Machine Learning (ML) pipelines to make your life that bit easier. Sounds cool, right? But do you need automated ML pipeline? What does this mean for machine learning?

Deployment is hard

Deploying software regularly and reliably is hard. Deploying software that utilises Machine Learning (ML) models regularly and reliably can be harder still. At the end of the day, the long-term value of your latest model pipeline will be determined (in part) by how much your company or your customers trust the resulting service, and how quickly you can address changing customer requirements by iterating on your pipeline.

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 business logic or modelling changes, while also ensuring those changes meet key performance criteria _before going into service with your stakeholders/customers.

Sounds cool, right? But do you need automated ML pipelines? Well that question largely boils down to the following question

  • Can you (as an ML practitioner) quickly and confidently release changes ‘into production’ with a single git commit?

In other words: can you release updates to your model and pipeline quickly and confidently? Do you have a systematic process for evaluating and testing the behaviour of your model and pipeline (including business logic, transformations etc) for each update you make?

If the answer to these sorts of questions is ‘no’ (or you’re unsure!), then this post is for you!

What you’ll learn

Here’s what you should get from this post:

  • An understanding of some of the key ideas and motivations behind the movement focussed on improving the level of reliability and automation in ML systems (often referred to as MLOps).
  • An understanding of how to automate a basic ML pipeline using GitHub Actions and Google Cloud. You’ll be using free services (or services with free tiers/introductory offers), so it shouldn’t cost you a dime!
  • A demo project that lets you deploy a Scikit-Learn Pipeline as a ‘production ready’ Serverless function! It’s a template project too, so you can use it as the basis for your own ML projects!

mlops github devops data-science

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