Our AI-ML Development Process
Source and preparing dataset
We articulate the problem early on and start working on sourcing and creating datasets. We establish data collection mechanisms, audit the data quality, format it, complete sampling process and data cleaning.
Coding the ML model
We use the raw data or datasets prepared in the earlier stage to test the possible Ai-based solution for a particular business problem. Our AI-consultants have loads of experience in creating an algorithm (coding on server) according to the business logic and then applying it to the datasets.
Evaluating and training the model
Evaluating the model with classification metrics to check accuracy and precision of the results from that model. Ultimately, the goal of any ML model is to learn from examples and generalize some degree of knowledge vis-a-vis the task we’re training it to perform.
Deploying the model
For most of our projects, we deploy machine learning models in batch prediction mode or as an on-demand prediction service. Both the model has its own merits, and the decision is taken as per the requirement.
Getting predictions from the model
A fit machine learning model takes inputs and makes a prediction. This means that we will provide all of the training data (previous dataset) to a learning algorithm (that we’ve created or an open source) and let the learning algorithm to discover the delineate between the inputs and the output class label that minimizes the prediction error.
Monitoring ongoing predictions
Machine learning models are dynamic in nature and may degrade over time after deployed to production. Hence, it is prudent to monitor both functional and operational factors of the ML model to check any data or feature issues.
Managing models and versions
The ML development model is quite complex – as it uses huge amounts of data, optimization, testing of multiple models, feature tuning etc. Hence, to make the research to be reproducible, we deploy proper version control tools to manage and track everything.
