Open-Source Deep Learning Frameworks and Visual Analytics
Deep learning is gaining grounds everywhere and because of its focused area in the domain of machine learning, it has been largely successful. Its methodologies are fixed and hence, its impact on analytics has been about other fields trying to incorporate it. Deep learning, along with artificial neural networks, can transform historical data into predictive value through an array of supervised algorithms. However, it is still extremely expensive and esoteric, which means you need to employ specialists by paying them high wages. So, avoid using them for small projects since deep learning may produce erratic results as well over-optimistic results.
Open source is crucial to data analytics and hence, understanding the core of such source is pivotal if you want to write an effective analytics model. In fact, some open source platforms are learning experiences for lesser experienced data scientists. Some of these sources dedicate themselves to specific tasks. For example, Google Vision is dedicated to image analytics; Bot from Microsoft specializes in creating chat bots etc. The experience of these behemoth figures in analyzing petabytes of widely varying data comes extremely handy when you use such analytical platforms.
The arrival of visual analytics
Neural networks can impact your decision making and hence, visual analytics must intervene to make it happen. Even for the laymen, visual analytics allow you to take decisions based on the data visualization. So, a businessman can now apply the model and find all the answers he or she needs to unearth complex business strategies. There are many analytical tools that a business professional can use if you are looking for the right answers.
Among them, embedded analytics is extremely popular because it is direct and you can implement it through the tool itself. Even a complete novice can use it and bring forth important decisions. Native integration, on the other hand, reaches out to external clusters of deep learning. Framework API is for more sophisticated user as you can access the analytics platform through a programming language like python. If you are an expert in handling more complex mechanisms, then you can use integration as a service scenario. Here, you use external clusters, but using a server-side tool.
For the most experienced users, there is the high-tech cloud service where you can access various pre-given models through the cloud framework of some big names in IT world. If you know what kind of problems you are working with and what kind of features you want to understand, you can use the given analytical tools without much expertise.
Streaming analytics for real time processing
It is only when analytics starts responding real time can its business value start truly surpassing its predecessors. With streaming analytics, you can now analyze new events to decide on the immediate trend as well as in the long run. However, the execution requirements are something worth attending to as the number of processes per unit time, the complicacy of the processes including the number of clusters as well as nodes can significantly impact how much is it plausible to apply real-time analytics.
All these technologies, when harnessed together, can significantly impact the big data scenario and bring success to many fronts. While deep learning is used for complex problems with clear problem statements, visual analytics ensures that a layman can manage the problems and come out with a decision or solution. Finally, streaming analytics will ensure that the whole process takes place in real time. All these analytical features have started integrating the neural network features and other machine learning features. The better the integration, the quicker it renders and the better will be the decision making.