What are machine learning pipelines and why are they important??
Machine Learning Pipelines play an important role in building production ready AI/ML systems. Using ML pipelines, data scientists, data engineers, and IT operations can collaborate on the steps involved in data preparation, model training, model validation, model deployment, and model testing.
How to automate machine learning pipeline automation for CT??
To automate the process of using new data to retrain models in production, you need to introduce automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management. The following figure is a schematic representation of an automated ML pipeline for CT. Figure 3. ML pipeline automation for CT.
What is the input and output of a machine learning pipeline??
For supervised learning, input is training data and labels and the output is model. Invoking fit method on pipeline instance will result in execution of pipeline for training data. This is illustrated in the code example in next section. Fig 1. Machine Learning Pipeline (Sklearn Implementation)
What is the last step in the machine learning pipeline??
The last step of the machine learning pipeline is often forgotten, but it is crucial to the success of data science projects. We need to close the loop. We can then measure the effectiveness and performance of the newly deployed model. During this step, we can capture valuable information about the performance of the model.