Building a Machine Learning model from scratch is a time consuming and complex process. The model has to undergo the process of training, testing, and finally deployment in a production environment. This helps to unwrap the potential it holds to solve real-world problems. In this article, let’s take a hypothetical object detection ML model and explore the steps followed to build, train, test, and deploy the same.
Data Collection
It is the process of acquiring data and identifying the input and output. The database of street images with vehicles and pedestrians is identified as the input and the annotated images are considered as the output. For instance, images having bounding boxes around pedestrians are considered as the output.
Before proceeding to collect data, you have to decide on the right data storage type and movement architecture. After collecting the necessary data required for the ML model building, the data has to be divided into three data sets via randomization. The best way to do this is to keep 80% data as a training set and the remaining 20% as test and validation data sets.
Model Building
Trying to overfit a model to a particular dataset may backfire as the model tends to work only under specific circumstances. If you train the model using images of sunny days, it may not be able to detect pedestrians in rainy day images or the ones taken from behind a window.
To cover all the important scenarios for each of the training datasets, it is best to identify the ground truth based on the human experience. You can make use of a panel of data annotators for creating the ground truth that in turn helps your model to achieve human-level accuracy.
Training & Testing
After the separation of datasets and identifying the ground truth, it is time for training the ML model with annotated data sets. During the ML model training, it is best to determine whether the incremental improvements achieved are worth the money spent.
It is not worth the money and time if there is just a one percent increase in accuracy after a thousand requests. If the additional time spent on model training has an impact of at least 1% on one million users or offers enhanced coverage of edge cases, then it is worth a try.
During the training process, the test data sets can be leveraged as the benchmark to know if the ML model can produce the desired results in the production environment or not.
Validation
After having trained the ML model appropriately, the validation datasets are leveraged to check if the ML model is overfitted or not. If the model is overfitted, then you may have to adjust the model over a couple of iterations or more for achieving accuracy and precision before moving it to the production environment.
About RightClick.AI
RightClick.AI offers high-quality data labeling services that will help you to train your ML model with greater accuracy. Reach out to us @ info@rightclick.ai for best-in-class datasets for your AI/ML projects.