ML & AI Automation

Artificial Intelligence & Machine Learning

AI & ML Are transforming business. Artificial Intelligence algorithms are used to bridge the gap between the humans and the machines and to make machines understand human data more easily. Artificial Intelligence is used to improve or automate processes of an enterprise in order to improve revenue or reduce costs or to completely redefine products.

We use artificial Intelligence algorithms to help machines learn themselves from the data they are exposed to and arrive at conclusions, simulating human intelligence. Our data scientists have expert knowledge in designing, implementing and integrating Artificial Intelligence applications specific to customers business.

Some of the expertise that we have
  • Chat Bots
  • Machine Learning solutions
  • Deep Learning & Neural Networks
  • Natural Language Processing
  • Predictive modelling
Data Acquisition

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 Preparations

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.

Hypothesis & Modelling

This is the process where we identify which model of Machine Learning suits the business needs.

Evaluation & Interpretation

In this stage we measure the performance of the Machine Learning model chosen in previous steps and hypothesis to check whether our interpretations match.

Deployment

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

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.