Challenges in Handling Data for Machine Learning

Whoever says that handling data is an easy job, hasn’t met a data scientist. Data scientists perform the core job of handling massive data sets and creating meaningful machine learning models. Often, data is unstructured and highly inaccurate; in that case, identifying the data essentials for the ML model is among the persistent issues faced by data scientists. Data scientists look for data sets that are clearly structured and properly trained. This helps them begin working on practical machine learning models focused on the business problem and AI applications that can deliver results.Training data forms the core in training machine learning models. While handling data, the data scientists face a lot of barriers. Extracting data from multiple sources, developing deep understanding of the business problem, collaborating with data engineers, adhering to data security guidelines and working out with unstructured data are some of the main challenges faced by any data scientist. Commonly, use of large as well as small data sets for training the ML models is carried out. Most of the time, for applying artificial intelligence, data scientists dig into all kinds of data sets and perform trial runs for identifying the best format of data set that ...


Read More on Datafloq