Machine Learning Lifecycle
The ML lifecycle defines sequential steps involved in data science projects that carry equal importance and are executed in a cyclic order
What is the Machine Learning Life Cycle?
The machine learning life cycle defines cyclic and sequential steps involved in data science projects. Each step in the ML lifecycle carries equal importance. ML professionals follow a defined sequence of lifecycle steps while executing ML projects.
ML Life Cycle Steps
Define problem objectives and AI approach
For any AI initiative, understanding the business context, problem scope, and use case is indispensable. A deeper understanding of problem context helps devise a better ML solution.
The success of AI adoption depends on the effective translation of problem objectives into an AI approach. Finding the right AI approach might be tricky. It requires a clear understanding of business problems and clarity on AI goals and expectations. Effective ML adoptions also involve resource and timeline planning, milestones, and KPIs understanding at this stage.
Data collection and exploration
Data constitutes the building block for any ML system. ML projects require data collection from appropriate resources. It can be challenged by data unstructuredness, improper formats, and lack of required quality with essential features. Data understanding and exploration by experts help analyze and use required features.
The data wrangling stage involves combining large data sets and preparing them in the required format. This step ensures data quality by addressing validation, missing values, noise, duplicates, and outliers.
This step is performed before building an ML model using the appropriate analytical technique. It involves over-sampling and under-sampling techniques to handle unbalanced datasets. Splitting the data into training and test data sets with proper ratios helps keep model accuracy in check. Data preprocessing helps model data for specific ML applications. It also maps all non-numerical data to numerical values to input into the neural network.
This step involves building a probabilistic model to describe the relationship between independent and dependent variables. It requires choosing appropriate ML techniques such as classification, regression, clustering, and more. It includes training a model on training datasets to uncover patterns, rules, and features.
Model building is followed by model validation steps that involve
- Testing and fine-tuning the model
- Model overfitting and model underfitting
- Evaluation metrics trade-off such as bias-variance and precision-recall
Model deployment and monitoring
Model deployment brings the model to a more complex production environment for real-time use. Deployed models are served with new live data streams for predictions. Deployment is followed by model monitoring to keep ML performance in check. The final stage of the ML lifecycle considers these aspects:
- Model scaling and deployments
- A/B testing
- KPI tracking
- Model performance monitoring
- Feedback loop
Taking the Next Steps
Machine learning lifecycle execution becomes easy with the right degree of automation for the stages involved. For example, instead of manually monitoring models for different KPIs and issues, it is preferable to set alerts using an AI Observability Platform like Censius.
The proper use of tools and technologies helps execute ML initiatives faster and agilely, ensuring better returns on your AI investments.