Similar to software development, machine learning has a lifecycle with defined steps that developers must follow to create machine learning systems. I guide you through each steps and jot down the steps.
- The formulation and comprehension of problems
To begin with, engineers should ascertain whether current business processes could benefit from Machine learning. Everyone is experimenting with machine learning these days. But not every issue is best or appropriate for machine learning to handle.
In this phase, 4 important definitions are
- The ethical approach to solving the business problem should be used.
- Define the input and outputs.
- Acceptable prediction error rate.
- Needs in terms of accuracy.
- Gathering and Preparing Data
In order to continue, the data will be sourced from a variety of sources, including internal systems, client sources, open sources, and third-party providers.
The data must then be curated, which includes labeling, annotating, eliminating extraneous information, excluding outliers, transforming the data, and inserting missing values.
- Model Development and Evaluation
After the necessary data has been set, it should be separated into three sections to start the execution.
- Training 80%
- Validation 10%
- Testing 10%
Choose the right method and follow the processes until the model is put into production, which includes fine-tuning, evaluating, and iterating.
- Installation and Upkeep of Models
After the model is made publicly available so that apps may be developed on top of it, a cadence for retraining and performance monitoring should be put up.
Conclusion
The iterative nature of the machine learning lifecycle guarantees that you will quickly develop a high-performing model.
