Machine learning
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The document discusses key considerations for building a machine learning pipeline. It recommends limiting the scope of each component, building reusable components, codifying testing into the pipeline, defining the order and flow of components through orchestration, and automating the pipeline when criteria are met. The overall message is that an effective pipeline incorporates data processing, modeling, testing, and automation in a modular and orchestrated manner.
Machine learning
- 1. What to Consider when Building a Machine Learning Pipeline? PRESENTED BY TYRONE SYSTEMS
- 2. Consider all the steps that go into producing your machine learning model. Start with how the data is collected and preprocessed, and work your way from there. It’s generally encouraged to limit each component’s scope to make it easier to understand and iterate. Build every step into reusable components.
- 3. Testing should be considered an inherent part of the pipeline. If you, in a manual process, do some sanity checks on how the input data and the model predictions should look like, you should codify this into a pipeline. A pipeline gives opportunities to be much, much more thorough with testing as you will not have to perform them manually each time. Don’t forget to codify tests into components.
- 4. There are many ways to handle the orchestration of a machine learning pipeline, but the principles remain the same. You define the order in which the components are executed and how inputs and outputs run through the pipeline. Orchestration of a machine learning
- 5. While building a pipeline already introduces automation as it handles the running of subsequent steps without human intervention, for many, the ultimate goal is also to automatically run the machine learning pipeline when specific criteria are met. Automate when needed
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