The international conference will gather leading experts in big data, data analysis and processing, and machine learning.
How is Automation Changing Healthcare?
The construction sector is one of the largest in the world economy, with about $10 trillion spent
Today adoption of machine learning technologies has become the key development trend for software companies. The use of AI determines product competitiveness on the global IT market. However, engineers who are just at the start of AI developments come across a great variety of questions concerning the choice of basic algorithmic approaches to problem solving. In this article, I’d like to describe the main features and use cases of AI in software development. Problems and Solutions
The emergence of standards in any technical field shows that the time of unique start-up solutions is coming to an end. Today these solutions are gradually being replaced by an industrial approach. New players are stepping in, they are not eager to take part in terminology controversy, but they would like to build rapport with partners and open their business to new consumers with whom they can speak a common language.
In our today’s blog, we are going to discuss the Big Data phenomenon from the viewpoint of software engineers and their top managers who face a choice: to develop competencies in Big Data and some other state-of-the-art technologies such as machine learning, and, thus, offer company services in this field; or not to rush and continue improving those skills that one succeeds in and that sell well. Whatever the answer to this question is, let’s try to figure out what is beyond the term “Big Data”.
In this post we will discuss two prevailing views of experts on Data Science that can be of interest for newcomers in this field. According to the first approach, the larger amount of data is obtained and processed, the more successful data extraction and analytics results are. Therefore, primary raw data has the greatest value. Another approach is that getting too much data makes it difficult to extract the required data, and only data that mostly fits the processing requirements should be selected.
You often read and hear similar questions from developers and students who are eager to participate in Big Data projects and invest their time or even capital in a study of tools that would allow them to start a project. A lot of materials are devoted to this subject, but today I will share my three years` experience in working with Big Data tools.