AI Technology Data Scientist Must Know

This AI technology is very attached to human life, because it can simplify daily activities. No wonder this technology attracts the attention of experts such as Data Scientist. As the world develops, many new technologies emerge. The technology that is currently being discussed by many people is Artificial Intelligence (AI). The following are some AI technologies that Data Scientists must know:


Machine learning operations or MLOps assist in the management, logistics and implementation of Machine Learning models between the operations team and Machine Learning researchers. MLOps aims to create reliable models of the future. So in its application, MLOps try to understand what works and what doesn’t work in a model.

Contrastive Learning

Contrastive Learning is a Machine Learning technique that is able to find things that are similar and not in a set of raw data (raw data). This technique can be used in image databases to find images that are similar to each other.


In data science, graphs are widely used to describe the relationship between one thing and another. The graph consists of nodes and edges that can capture complex relationships. Where nodes include subjects, objects, or places, while edges include relationships between nodes.


Transformers is a neural network architecture that handles sequential input data. It is used extensively in language models, including in language translation and text-to-text applications.

Efficient Tool

Before conducting a trial, leaders and data science are required to collect tools. However, many technology vendors are trying to complement their tools, so that companies no longer use different tools from various sources. Of course, this allows data scientists to use one platform and not have to bother moving data and models from various different tools,