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Since inception, we have successfully implemented comprehensive Machine Learning and Deep Learning solutions in production across Insurance, Healthcare, Education , Retail, FMCG & Manufacturing sectors.
Data acquisition or data engineering is the process of collecting and preparing raw data for analysis. It involves gathering data from various sources and converting it into a format that can be easily analyzed.
Data Processing or feature engineering is the step in machine learning where raw data is transformed into features that can be used in a model. It involves selecting, cleaning, and transforming the data to create meaningful features that capture relevant information.
Model building, is the process of creating a machine learning model using the selected features and algorithm. This step involves training the model on the prepared data, tuning the model parameters to improve its performance, and evaluating the model using appropriate metrics.
ML model execution applies the trained model to new data to make predictions or classifications. It involves passing input data through the model and receiving the corresponding output. Model execution is often automated and integrated into larger systems, allowing for real-time or near-real-time predictions to be made.
ML model deployment is the process of making the trained model available for use in a production environment. This step involves integrating the model into a larger system, creating an API or web service for accessing the model, and setting up monitoring and maintenance processes.
Managing and governing information requires a comprehensive approach that includes information governance and data management. Data management includes tasks such as data administration, security, and privacy, which are crucial for protecting sensitive information
Discover and Review AI capabilities and define priorities and success factors to generate AI/ ML use cases to address key business pain points.
Identifying quick wins by Proof of Concepts to demonstrate potential capabilities of AI/ ML using real outcomes to solve real problems.
Define required data sources and data mining, preparation, and refinement activities i.e. Data Cleansing, Data Lakes, Data Governance and Data Management.
AI strategy implementation by adopting AI best practices to build, deploy, and operate AI/ML solutions at scale
