Need for Machine Learning Pipelines
• Machine learning pipelines are crucial for sustainable machine learning systems.
• But what good do they bring? What are the benefits that we get from them?
Let's discuss ↓
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Although building automated machine learning pipelines can lead to several little benefits, here are five of the major ones:
1. Easier Maintenance
2. Bug Prevention
3. Proper Trails
4. No Glue-Code
5. Time-Saving
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1. Easier Maintenance
• Building pipelines require one time creation and then can be used for subsequent deployments.
• This allows for easy and consistent upgradation of models. Lets data scientists focus more on the experiments and improving the models.
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2. Bug Prevention
• Machine Learning pipelines do everything with proper versioning and monitoring.
• The pipeline steps remain same until explicitly changed making sure no accidental updation takes place leading to untraceable bugs.
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3. Proper Trails
• In many scenarios we may want to reproduce previous results which require accurate records.
• In pipelines all the data, hyper parameters, models can be traced to the details of the process they were created from which are hard to maintain explicitly.
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4. No Glue-Code
• Glue code refers to the manual code required to connect steps in ML process flow.
• With standard pipelines, this code is reduced to standard and reusable code which connects the pipeline components.
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5. Time-Saving
• Machine learning pipelines save a lot of time by making simpler process to update and reproduce models.
• They are essential in the projects that involve a customer base or is part of an application.
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Machine Learning Pipelines are of paramount importance in the real world production applications.
Stay tuned for more on production pipelines.
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