Building a Machine Learning model is just not enough. The ML model has to be trained, tested and deployed in a production environment to understand what value it provides in solving the real-world problems. In this article, we will explore the steps to build, train, test and deploy an ML model using a hypothetical object-detection ML model.
Data Acquisition and Gathering
This step involves acquiring specific data and deciding on the input and output. Available street images with pedestrians are considered as input and images with annotations are the output. Let’s consider images with bounding boxes identifying the pedestrians as the output.
Before acquiring the data, you need to decide on the right type of data storage and movement architecture. After acquiring the data necessary for building an ML model, you will have to divide it into three different data sets via randomization. The right way of doing this is to keep 80% as the training set and rest 20% as your validation and test data sets.
Try not to overfit the model to anyone particular data set as it may work only under certain circumstances. For instance, it may not be able to detect pedestrians in photos which are taken from behind a window or may not detect pedestrians in rainy day photos if the model is trained using images of sunny days.
It is best to establish the ground truth for the training data based on human experience which helps to ensure good coverage of all the important scenarios in each of the datasets. A panel of data annotators will help you in creating ground truth which helps you to achieve human-level accuracy with your model.
Training & Testing
After separating the datasets and establishing the ground truth, it’s time to train the ML model with labeled data sets. When training the ML model, one has to determine whether the incremental improvements are worth the money.
One percent increased accuracy after a thousand requests are not worthy enough. If the increased time spent on training has at least a 1% impact on one million users or provides improved coverage for edge cases, then it’s worth a try.
Through the course of training, it is best to leverage the test data sets as the benchmark to test whether the ML model will work in production or not.
After your ML model is appropriately trained, it’s time to leverage the validation data to find out whether you have over fitted your ML model or not. In that case, you will have to do more iterative changes to the model before moving it to production.
About Data Labeler
Data Labeler offers high-quality data labeling services in New York which will help you to train your ML model with great accuracy. Reach out to us @ firstname.lastname@example.org for best-in-class datasets for your computer vision projects.