In the world of data science, efficient and accurate labeling of data is crucial to ensuring successful Machine Learning applications. While an array of online tools exist to make data labeling easier, they often involve managing multiple vendor relationships, hosting external datasets on an alien server environment with limited accessibilities,

A machine learning system learns from historical data, builds a predictive model, and predicts its output whenever new data is obtained. The accuracy of the predicted output depends on the amount of data. Massive amounts of data help build better models that more accurately predict the output. Suppose you have

Knowing where the other vehicles are on the road and being able to anticipate where they’re heading next and also determining things like how far away they are, which way they’re going, and how fast they’re moving is essential in an autonomous vehicle. Much the same way