Artificial Intelligence & Machine Learning
Some of the expertise that we have
- Chat Bots
- Machine Learning solutions
- Deep Learning & Neural Networks
- Natural Language Processing
- Predictive modelling
Data Acquisition (DAQ) is the process to collect the data from different sources like person who has data or from electronic devices, sensors etc., for particular time or time range.
Data preparation is the process of where our Data Scientists clean and transform raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data.
This is the process where we identify which model of Machine Learning suits the business needs.
In this stage we measure the performance of the Machine Learning model chosen in previous steps and hypothesis to check whether our interpretations match.
After evaluation and interpretation, we need to deploy the model to preproduction environment for testing and may need to recode or do some tweaks to the model before we deploy it to production environment.
Optimization is the final phase in Data Science project life cycle. Optimization is required when the model performance degrades due to increase in data or we may need to make changes to our algorithms because of new needs of business, then we make changes to existing model and redeploy. This is part of regular application maintenance.