Machine Learning Lifecycle Management is the process of managing all aspects of creating, deploying, and maintaining machine learning models. It involves several stages including defining the problem, data preparation, model development, and deployment and monitoring.
Imagine you are baking a cake. First, you would decide what kind of cake you want to bake, gather all the ingredients (your data), mix them together following a recipe (this is your model), bake it (train your model), and finally taste and check if it’s good (this is testing your model). If it’s not good, you would adjust your recipe and try again. This whole process, from deciding what cake to make to tasting it, is like the lifecycle of a machine learning model. It doesn’t stop there, though. You would also want to share your cake with others and continually check if it’s still good over time (this is deploying and monitoring your model). That’s what Machine Learning Lifecycle Management is about - managing this whole process.
Machine Learning Lifecycle Management (MLLCM) encapsulates the entire process of a machine learning project including defining the problem, preparing and managing the data, selecting and training the model, testing and validating the model, deploying the model and monitoring its performance over time.
In the first stage of MLLCM, problem definition, clarity about what problem the machine learning model is expected to solve is paramount. This phase also includes compiling initial requirements.
Data preparation and management involves cleaning, transforming, and partitioning data into suitable formats for machine learning. Missing values, irrelevant entries and corrupt data points are dealt with in this stage.
Model selection and training targets the core operations of machine learning. Several models may be trained and tweaked depending on the problem and the data available. The appropriate model is selected depending on the nature of the problem, the target variable, and the data.
Testing and validating the model becomes critical in the lifecycle. This stage assesses if the chosen model provides satisfactory outcomes. It validates whether the results correspond closely to observed data when applied to unseen test data. Measures of model performance such as accuracy or precision are calculated.
Deploying the model involves integrating the liked model into the existing production environment so it can start providing predictions on real, live data. This requires constant compatibility checks and successful integration of the model into the system following specific criteria.
The monitoring and maintenance stage pertains to observing the performance of the deployed model over time. Models may require reiteration or retraining if the performance declines or the environment changes.
The goal of MLLCM is to have a structured way to handle ML projects, allowing for better visibility, version control, experiment tracking, reproducibility, and resource optimization.