Machine Learning (ML), a part of Artificial Intelligence (AI), can process large volumes of data and predict meaningful outputs that humans find difficult to do. ML avoids human errors and predicting anomalies. It can help businesses make accurate decisions quickly and achieve high productivity through process automation.
ML can unleash the hidden strength in user data and derive intelligence out of every customer interaction. It can be used for content creation and extraction to optimize pages for easy readability.
ML can help in in-depth customer segmentation including geo-location-based product personalization. It can mine crucial data out of sales calls and help in sales enablement for better follow up.
Our ML project approach is highly iterative and consists of building ML models through the ML lifecycle. This iterative approach ends only when we achieve a satisfactory level of performance in our ML model. The project checklist of our ML model consists of the following:
We understand the requirements and define the scope of the project and determine its feasibility. Our experts explore the ML model parameters based on the requirements to set up the project codebase.
Collecting and Labeling
We validate the quality of data available and then split them into train, test and validate. We revisit the task definition and project scope to cross-check whether the data we have collected is sufficient for the task.
Exploration and Refinement
We build a simple model to check the performance and then find a State-of-the-Art (SoTA) model that suits your requirements and generate results. Next, we perform hyperparameter tuning and debug the model as its complexity increases and analyze errors to identify common failure modes.
Testing and Evaluation
Testing includes model interference functionality and performance on the validation data. The model has to be evaluated on all sets of observations as per the project scope.
The ML model is deployed to a small userbase and after a specific time, to all users. We ensure that any specific ML model can be rolled back to the previous version in case of errors. Parallelly, we monitor the live data and the prediction distributions.
We ensure that the ML model doesn’t stale by periodically retraining the model. In case the model has to be handled by a new team, we educate them about the model functionalities.
Why Choose Ziffity?
Ziffity has hands-on experience in ML and has developed custom solutions specific to each business model. We have deployed ML for various data analytics measurements such as Customer Segmentation, Cart Abandonment reasons, Inventory management & forecasting and anticipatory shipping and planning. We begin with understanding your data, prepare the data for easy processing and analysis, build ML models, evaluate for accuracy and then deploy them.