Global Big Data Conference


Data science has reached its peak through automation. All the phases of a data science project -- like data cleaning, model development, model comparison, model validation, and deployment -- are fully automated and can be executed in minutes, which earlier would have taken months. Machine learning (ML) continuously works to tweak the model to improve predictions. It's extremely critical to set up the right data pipeline to have a continuous flow of new data for all your data science, artificial intelligence (AI), ML, and decision intelligence projects. Decision intelligence (DI) is the next major data-driven decision-making technique for disruptive innovation after data science. Futuristic – Models ML outcomes to predict social, environmental, and business impact.